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256 lines
11 KiB
Markdown
256 lines
11 KiB
Markdown
# 使用 Anaconda
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[Anaconda](http://www.continuum.io/downloads)是一个很好用的Python IDE，它集成了很多科学计算需要使用的**python**第三方工具包。
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## conda 的使用
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根据自己的操作系统安装好[Anaconda](http://www.continuum.io/downloads)后，在命令行下输入：
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```py
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conda list
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```
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可以看已经安装好的**python**第三方工具包，这里我们使用 `magic` 命令 `%%cmd` 在 `ipython cell` 中来执行这个命令：
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In [1]:
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```py
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!conda list
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```
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```py
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# packages in environment at C:\Anaconda:
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#
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_license 1.1 py27_0
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alabaster 0.7.3 py27_0
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anaconda 2.3.0 np19py27_0
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argcomplete 0.8.9 py27_0
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astropy 1.0.3 np19py27_0
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babel 1.3 py27_0
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backports.ssl-match-hostname 3.4.0.2 <pip>
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basemap 1.0.7 np19py27_0
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bcolz 0.9.0 np19py27_0
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beautiful-soup 4.3.2 py27_1
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beautifulsoup4 4.3.2 <pip>
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binstar 0.11.0 py27_0
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bitarray 0.8.1 py27_1
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blaze 0.8.0 <pip>
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blaze-core 0.8.0 np19py27_0
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blz 0.6.2 np19py27_1
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bokeh 0.9.0 np19py27_0
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boto 2.38.0 py27_0
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bottleneck 1.0.0 np19py27_0
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cartopy 0.13.0 np19py27_0
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cdecimal 2.3 py27_1
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certifi 14.05.14 py27_0
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cffi 1.1.0 py27_0
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clyent 0.3.4 py27_0
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colorama 0.3.3 py27_0
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conda 3.17.0 py27_0
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conda-build 1.14.1 py27_0
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conda-env 2.4.2 py27_0
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configobj 5.0.6 py27_0
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cryptography 0.9.1 py27_0
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cython 0.22.1 py27_0
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cytoolz 0.7.3 py27_0
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datashape 0.4.5 np19py27_0
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decorator 3.4.2 py27_0
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docutils 0.12 py27_1
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dynd-python 0.6.5 np19py27_0
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enum34 1.0.4 py27_0
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fastcache 1.0.2 py27_0
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flask 0.10.1 py27_1
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funcsigs 0.4 py27_0
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geopy 1.11.0 <pip>
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geos 3.4.2 3
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gevent 1.0.1 py27_0
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gevent-websocket 0.9.3 py27_0
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greenlet 0.4.7 py27_0
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grin 1.2.1 py27_2
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h5py 2.5.0 np19py27_1
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hdf5 1.8.15.1 2
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idna 2.0 py27_0
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ipaddress 1.0.7 py27_0
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ipython 3.2.0 py27_0
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ipython-notebook 3.2.0 py27_0
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ipython-qtconsole 3.2.0 py27_0
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itsdangerous 0.24 py27_0
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jdcal 1.0 py27_0
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jedi 0.8.1 py27_0
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jinja2 2.7.3 py27_2
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jsonschema 2.4.0 py27_0
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launcher 1.0.0 1
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libpython 1.0 py27_1
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llvmlite 0.5.0 py27_0
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lxml 3.4.4 py27_0
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markupsafe 0.23 py27_0
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matplotlib 1.4.3 np19py27_1
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menuinst 1.0.4 py27_0
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mingw 4.7 1
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mistune 0.5.1 py27_1
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mock 1.3.0 py27_0
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multipledispatch 0.4.7 py27_0
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networkx 1.9.1 py27_0
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nltk 3.0.3 np19py27_0
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node-webkit 0.10.1 0
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nose 1.3.7 py27_0
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numba 0.19.1 np19py27_0
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numexpr 2.4.3 np19py27_0
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numpy 1.9.2 py27_0
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odo 0.3.2 np19py27_0
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openpyxl 1.8.5 py27_0
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owslib 0.9.0 py27_0
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pandas 0.16.2 np19py27_0
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patsy 0.3.0 np19py27_0
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pbr 1.3.0 py27_0
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pep8 1.6.2 py27_0
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pillow 2.9.0 py27_0
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pip 7.1.2 py27_0
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ply 3.6 py27_0
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proj4 4.9.1 py27_1
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psutil 2.2.1 py27_0
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py 1.4.27 py27_0
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pyasn1 0.1.7 py27_0
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pycosat 0.6.1 py27_0
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pycparser 2.14 py27_0
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pycrypto 2.6.1 py27_3
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pyepsg 0.2.0 py27_0
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pyflakes 0.9.2 py27_0
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pygments 2.0.2 py27_0
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pyopenssl 0.15.1 py27_1
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pyparsing 2.0.3 py27_0
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pyqt 4.10.4 py27_1
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pyreadline 2.0 py27_0
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pyshp 1.2.1 py27_0
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pytables 3.2.0 np19py27_0
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pytest 2.7.1 py27_0
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python 2.7.10 0
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python-dateutil 2.4.2 py27_0
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pytz 2015.4 py27_0
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pywin32 219 py27_0
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pyyaml 3.11 py27_2
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pyzmq 14.7.0 py27_0
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requests 2.7.0 py27_0
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rope 0.9.4 py27_1
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runipy 0.1.3 py27_0
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scikit-image 0.11.3 np19py27_0
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scikit-learn 0.16.1 np19py27_0
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scipy 0.16.0 np19py27_0
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setuptools 18.1 py27_0
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shapely 1.5.11 nppy27_0
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six 1.9.0 py27_0
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snowballstemmer 1.2.0 py27_0
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sockjs-tornado 1.0.1 py27_0
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sphinx 1.3.1 py27_0
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sphinx-rtd-theme 0.1.7 <pip>
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sphinx_rtd_theme 0.1.7 py27_0
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spyder 2.3.5.2 py27_0
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spyder-app 2.3.5.2 py27_0
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sqlalchemy 1.0.5 py27_0
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ssl_match_hostname 3.4.0.2 py27_0
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statsmodels 0.6.1 np19py27_0
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sympy 0.7.6 py27_0
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tables 3.2.0 <pip>
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theano 0.7.0 <pip>
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toolz 0.7.2 py27_0
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tornado 4.2 py27_0
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ujson 1.33 py27_0
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unicodecsv 0.9.4 py27_0
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werkzeug 0.10.4 py27_0
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wheel 0.24.0 py27_0
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xlrd 0.9.3 py27_0
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xlsxwriter 0.7.3 py27_0
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xlwings 0.3.5 py27_0
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xlwt 1.0.0 py27_0
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zlib 1.2.8 0
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```
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第一次安装好 [Anaconda](http://www.continuum.io/downloads) 以后，可以在命令行输入以下命令使 [Anaconda](http://www.continuum.io/downloads) 保持最新：
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```py
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conda update conda
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conda update anaconda
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```
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conda 是一种很强大的工具，具体用法可以参照它的[文档](http://conda.pydata.org/docs/)。
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也可以参考它的 [cheat sheet](http://conda.pydata.org/docs/_downloads/conda-cheatsheet.pdf) 来快速查看它的用法。
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可以使用它来安装，更新，卸载第三方的 **python** 工具包：
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```py
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conda install <some package>
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conda update <some package>
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conda remove <some package>
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```
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在安装或更新时可以指定安装的版本号，例如需要使用 `numpy 1.8.1`：
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```py
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conda install numpy=1.8.1
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conda update numpy=1.8.1
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```
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查看 `conda` 的信息：
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```py
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conda info
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```
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In [2]:
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```py
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!conda info
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```
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```py
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Current conda install:
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platform : win-64
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conda version : 3.17.0
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conda-build version : 1.14.1
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python version : 2.7.10.final.0
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requests version : 2.7.0
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root environment : C:\Anaconda (writable)
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default environment : C:\Anaconda
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envs directories : C:\Anaconda\envs
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package cache : C:\Anaconda\pkgs
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channel URLs : https://repo.continuum.io/pkgs/free/win-64/
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https://repo.continuum.io/pkgs/free/noarch/
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https://repo.continuum.io/pkgs/pro/win-64/
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https://repo.continuum.io/pkgs/pro/noarch/
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config file : None
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is foreign system : False
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```
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一个很棒的功能是 `conda` 可以产生一个自定义的环境，假设在安装的是 **Python 2.7** 的情况下，想使用 **Python 3.4**，只需要在命令行下使用 `conda` 产生一个新的环境：
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```py
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conda create -n py34 python=3.4
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```
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这里这个环境被命名为 `py34` ，可以根据喜好将 `py34` 改成其他的名字。
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使用这个环境时，只需要命令行下输入：
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```py
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activate py34 #(windows)
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source activate py34 #(linux, mac)
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```
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此时，我们的 **Python** 版本便是 **`python 3.4`**了。
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## spyder 编辑器
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`Anaconda` 默认使用的编辑器是 `spyder`，可以在命令行下输入：
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```py
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spyder
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```
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来进入这个编辑器，具体使用方法不做介绍。 |