blocks|key|1068403|text|更新2015：现在我总是推荐蟒蛇。它包含大量的Python包，用于科学计算、数据科学、web开发等。它还提供了一个优秀的环境工具conda，它允许在环境之间轻松切换，甚至在Python2和Python3之间切换。它还会在新版本的包发布后迅速更新，您可以只需执行conda+update+packagename来更新它。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|CODE|entityRanges|data|1068404|下面的原始答案|1068405|在Windows上，编译数学软件包很复杂，所以我认为只有当你只对Python感兴趣，而没有其他软件包时，手动安装才是一个可行的选择。|1068406|因此最好选择EPD+(现在的Canopy)或Anaconda。|1068407|蟒蛇有大约270个软件包，包括最重要的科学应用程序和数据分析，即NumPy，SciPy，Pandas，IPython，matplotlib，Scikit-learn。因此，如果这对你来说足够了，我会选择Anaconda。|1068408|相反，如果你对其他软件包感兴趣，甚至更感兴趣，如果你使用任何Instead软件包(例如Chaco对于实时数据可视化非常有用)，那么EPD/Canopy可能是更好的选择。学术版在基础安装中有更多的包，在存储库中有更多的包。Anaconda也包括查科。|1068409|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/NumPy|1|http://en.wikipedia.org/wiki/SciPy|2|https://en.wikipedia.org/wiki/Pandas_%2528software%2529|3|http://en.wikipedia.org/wiki/IPython|4|http://en.wikipedia.org/wiki/Matplotlib|5|https://en.wikipedia.org/wiki/Scikit-learn|6|http://code.enthought.com/chaco/^0|0|6|1S|5|3M|O|0|3|4|0|0|1U|W|6|0|0|V|0|0|32|W|5|0|12|5|1|18|6|2|1F|7|3|1N|A|4|1Y|C|5|0|0|3G|17|5|6|0^^$0|@$1|2|3|4|5|6|7|1A|8|@$9|1B|A|1C|B|C]|$9|1D|A|1E|B|D]|$9|1F|A|1G|B|D]]|E|@]|F|$]]|$1|G|3|H|5|6|7|1H|8|@$9|1I|A|1J|B|C]]|E|@]|F|$]]|$1|I|3|J|5|6|7|1K|8|@$9|1L|A|1M|B|C]|$9|1N|A|1O|B|D]]|E|@]|F|$]]|$1|K|3|L|5|6|7|1P|8|@$9|1Q|A|1R|B|C]]|E|@]|F|$]]|$1|M|3|N|5|6|7|1S|8|@$9|1T|A|1U|B|C]]|E|@$9|1V|A|1W|1|1X]|$9|1Y|A|1Z|1|20]|$9|21|A|22|1|23]|$9|24|A|25|1|26]|$9|27|A|28|1|29]|$9|2A|A|2B|1|2C]]|F|$]]|$1|O|3|P|5|6|7|2D|8|@$9|2E|A|2F|B|C]]|E|@$9|2G|A|2H|1|2I]]|F|$]]|$1|Q|3|-4|5|6|7|2J|8|@]|E|@]|F|$]]]|R|$S|$5|T|U|V|F|$W|X]]|Y|$5|T|U|V|F|$W|Z]]|10|$5|T|U|V|F|$W|11]]|12|$5|T|U|V|F|$W|13]]|14|$5|T|U|V|F|$W|15]]|16|$5|T|U|V|F|$W|17]]|18|$5|T|U|V|F|$W|19]]]]

Update 2015: Nowadays I always recommend Anaconda. It includes lots of Python packages for scientific computing, data science, web development, etc. It also provides a superior environment tool, <code>conda</code>, which allows to easily switch between environments, even between Python 2 and 3. It is also updated very quickly as soon as a new version of a package is released, and you can just do <code>conda update packagename</code> to update it.

Original answer below:

On Windows, what is complicated is to compile the math packages, so I think a manual install is a viable option only if you are interested only in <code>Python</code>, without other packages.

Therefore better chose either EPD (now Canopy) or Anaconda.

Anaconda has around 270 packages, including the most important for most scientific applications and data analysis, that is, <a href="http://en.wikipedia.org/wiki/NumPy" rel="noreferrer">NumPy</a>, <a href="http://en.wikipedia.org/wiki/SciPy" rel="noreferrer">SciPy</a>, <a href="https://en.wikipedia.org/wiki/Pandas_%28software%29" rel="noreferrer">Pandas</a>, <a href="http://en.wikipedia.org/wiki/IPython" rel="noreferrer">IPython</a>, <a href="http://en.wikipedia.org/wiki/Matplotlib" rel="noreferrer">matplotlib</a>, <a href="https://en.wikipedia.org/wiki/Scikit-learn" rel="noreferrer">Scikit-learn</a>.
So if this is enough for you, I would choose Anaconda.

Instead, if you are interested in other packages, and even more if you use any of the Enthought packages (<a href="http://code.enthought.com/chaco/" rel="noreferrer">Chaco</a> for example is very useful for realtime data visualization), then EPD/Canopy is probably a better choice. The Academic version has a larger number of packages in the base install, and many more in the repository. Anaconda also includes Chaco.

blocks|key|5968388|text|去年，我尝试了各种Windows发行版，试图找到适合我工作环境的发行版(在代理之后，但无法访问代理配置)。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5968389|以下是我的经验反馈：|5968390|环保署/树冠：我们有环保署的许可证，但它太旧了，由于奇怪的代理情况，我们无法更新。为了添加一些包(比如最新版本的xlrd/xlwt)，我从源代码编译。为了更新SciPy和NumPy，我使用了http://www.lfd.uci.edu/~gohlke/pythonlibs/的预编译安装程序，但它有时会破坏兼容性。我喜欢拥有一个完全配置的Py2exe和Cython，而且它开箱即用。|offset|length|style|BOLD|5968391|过了一段时间，我尝试安装了免费版本的Canopy，但它没有Cython和py2exe，也没有我需要的一些特定的高级软件包，所以我从来没有真正使用过它。我的一些同事购买了完整的Canopy许可证，但我们仍然不确定他们将如何更新……|5968392|Python+Python(x，y):不想为许可证而苦苦挣扎，我在家里安装了(x，y)。我现在注意到的唯一缺点是标准安装需要你选择你想要的包。这既是一个优点，也是一个缺点，因为我不能确定我的客户端在安装时是否会有与我完全相同的配置。(可以在Python(x，y)中安装installed工具套件。)在使用Python(x，y)一段时间后，我才注意到我安装了32位版本。虽然在他们的网站上还不清楚，但似乎到2015年7月为止他们还没有64位版本。我将卸载它并获得一个64位的发行版。|5968393|+Anaconda+:当我第一次写这篇文章时，Anaconda似乎还没有足够的包。几年后，它看起来好多了，我要试一试！|5968394|手册：为了避免与我们的旧版本的版本兼容性问题，我最终使用了手动安装Python，并从上面链接的LFD网站添加了额外的软件包。它工作得很好，但我仍然建议需要高级软件包(如GDAL或PyFITS)的新用户使用Canopy。|5968395|摘要：如果你想购买树冠，请获得完整的许可证(学术版或购买版)。否则，使用Python(x，y)，结果将是相同的。|5968396|Ubuntu上的：不需要发行版。它都是相对较新的(%2B/-+6个月是可以容忍的)和预编译的。您只需要执行sudo+apt-get+install+python+python-scipy，它就在那里！最高级的包也在那里。|CODE|5968397|entityMap|0|LINK|mutability|MUTABLE|url|http://www.python-excel.org/|1|http://en.wikipedia.org/wiki/SciPy|2|http://en.wikipedia.org/wiki/NumPy|3|http://www.lfd.uci.edu/~gohlke/pythonlibs/|4|https://en.wikipedia.org/wiki/Py2exe|5|http://en.wikipedia.org/wiki/Cython|6|https://en.wikipedia.org/wiki/GDAL|7|https://en.wikipedia.org/wiki/Astropy#Core_functionality^0|0|0|0|7|1K|9|0|27|5|1|2D|5|2|2N|16|3|4P|6|4|4W|6|5|0|0|12|5M|0|0|1N|0|0|31|2C|4|6|2H|6|7|0|0|1K|0|0|30|1F|14|0^^$0|@$1|2|3|4|5|6|7|1I|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1J|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|1K|8|@$F|1L|G|1M|H|I]]|9|@$F|1N|G|1O|1|1P]|$F|1Q|G|1R|1|1S]|$F|1T|G|1U|1|1V]|$F|1W|G|1X|1|1Y]|$F|1Z|G|20|1|21]|$F|22|G|23|1|24]]|A|$]]|$1|J|3|K|5|6|7|25|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|26|8|@$F|27|G|28|H|I]]|9|@]|A|$]]|$1|N|3|O|5|6|7|29|8|@$F|2A|G|2B|H|I]]|9|@]|A|$]]|$1|P|3|Q|5|6|7|2C|8|@$F|2D|G|2E|H|I]]|9|@$F|2F|G|2G|1|2H]|$F|2I|G|2J|1|2K]]|A|$]]|$1|R|3|S|5|6|7|2L|8|@$F|2M|G|2N|H|I]]|9|@]|A|$]]|$1|T|3|U|5|6|7|2O|8|@$F|2P|G|2Q|H|I]|$F|2R|G|2S|H|V]]|9|@]|A|$]]|$1|W|3|-4|5|6|7|2T|8|@]|9|@]|A|$]]]|X|$Y|$5|Z|10|11|A|$12|13]]|14|$5|Z|10|11|A|$12|15]]|16|$5|Z|10|11|A|$12|17]]|18|$5|Z|10|11|A|$12|19]]|1A|$5|Z|10|11|A|$12|1B]]|1C|$5|Z|10|11|A|$12|1D]]|1E|$5|Z|10|11|A|$12|1F]]|1G|$5|Z|10|11|A|$12|1H]]]]

I have tried various Windows distributions in the last year, trying to find one sutable for my work environment (behind a proxy, but without access to proxy configuration).

Here is my feedback from experience:

EPD/Canopy:
We had a license of EPD, but it was old and we were unable to update becasue of the weird proxy situation. In order to add some packages (such as recent version of <a href="http://www.python-excel.org/" rel="nofollow">xlrd/xlwt</a>), I compiled from source. To update <a href="http://en.wikipedia.org/wiki/SciPy" rel="nofollow">SciPy</a> and <a href="http://en.wikipedia.org/wiki/NumPy" rel="nofollow">NumPy</a>, I used the precompiled installer from <a href="http://www.lfd.uci.edu/~gohlke/pythonlibs/" rel="nofollow">http://www.lfd.uci.edu/~gohlke/pythonlibs/</a>, but it would sometimes screw up compatibility. I loved having a fully configured <a href="https://en.wikipedia.org/wiki/Py2exe" rel="nofollow">Py2exe</a> and <a href="http://en.wikipedia.org/wiki/Cython" rel="nofollow">Cython</a>, and it simply worked out of the box.

After a while, I tried installing the free version of Canopy, but it lacks Cython and py2exe and some specific advanced packaged I needed, so I never really used it.
Some of my colleagues bought the full Canopy license, but we're still not sure how they're going to update...

Python(x,y):
Not wanting to struggle with licenses, I installed Python(x,y) at home. The only downside I noticed right now is that the standard installation requires you to select which packages you want. It's both a good and a bad point, because I can't be sure that my clients will have the exact same configuration as I do when I install. (The Enthought tool suite can be installed in Python(x,y).)
After using Python(x,y) for a while, I just noticed I installed the 32 bit version. Although it is not clear on their website, it seems they don't have a 64 bit version as of July 2015. I'm going to uninstall it and get a 64 bit distribution.

Anaconda:
When I first wrote this, Anaconda didn't seem to have enough packages yet. A couple of years later, it seems much better, I'm going to give it a try!

Manual:
In order to avoid version compatibility issues with our old EPD version, I ended up using manual Python installation and adding additional packages from the LFD website linked above. It works great, but I would still suggest Canopy to a new user who requires advanced packages (like <a href="https://en.wikipedia.org/wiki/GDAL" rel="nofollow">GDAL</a> or <a href="https://en.wikipedia.org/wiki/Astropy#Core_functionality" rel="nofollow">PyFITS</a>).

Summary: If you go for Canopy, get the full licence (Academic or purchased). Else, go with Python(x,y), it will end up being the same.

On Ubuntu:
No need for a distribution. It's all relatively recent (+/- 6 months is tolerable) and pre-compiled. You just need to execute <code>sudo apt-get install python python-scipy</code> and it's there! Most advanced packages are there as well.

blocks|key|2616034|text|其他答案很好地涵盖了这一点，所以我只想评论一下还没有人提到的一个特定方面。它可能是相当小众的，但对于Linux系统下的一些人来说，它可能会建立或破坏Anaconda或Canopy：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2616035|Anaconda+Python构建使用的是UCS4+Unicode模式，而Unicode使用的是UCS2。|2616036|这实际上意味着，如果你依赖于任何不能自己编译的扩展(例如预编译的专有库)，如果它们碰巧不是为具有相同模式的Python版本构建的，你迟早会遇到类似于undefined+symbol:+PyUnicodeUCS4_AsUTF8String的错误。|offset|length|style|CODE|2616037|根据PEP+0513的说法，UCS4目前似乎更受欢迎和推荐。此外，整个UCS兼容性问题似乎只影响2.x和低于3.3的版本。|2616038|entityMap|0|LINK|mutability|MUTABLE|url|https://www.python.org/dev/peps/pep-0513/#ucs-2-vs-ucs-4-builds^0|0|0|22|18|0|2|8|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|V|8|@$F|W|G|X|H|I]]|9|@]|A|$]]|$1|J|3|K|5|6|7|Y|8|@]|9|@$F|Z|G|10|1|11]]|A|$]]|$1|L|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|M|$N|$5|O|P|Q|A|$R|S]]]]

The other answers cover the ground quite nicely, so I just want to remark on one particular aspect that nobody has mentioned yet. It is probably fairly niche, but it may potentially make or break Anaconda or Canopy for some people under Linux systems:

Anaconda Python builds use the UCS4 Unicode mode, whereas Enthought Canopy uses UCS2.

What this means in practical terms is that if you rely on any extensions which you cannot compile yourself for whatever reason (e.g. pre-compiled proprietary libraries), if they happen not to be built for a Python version with the same mode, you may sooner or later run into errors that look something like <code>undefined symbol: PyUnicodeUCS4_AsUTF8String</code>.

According to <a href="https://www.python.org/dev/peps/pep-0513/#ucs-2-vs-ucs-4-builds" rel="nofollow">PEP 0513</a>, UCS4 seems to currently be more popular and recommended. Also, the whole UCS compatibility issues seem to only affect 2.x and &lt; 3.3 versions.

blocks|key|491359|text|我使用Anaconda很多年了，并且非常喜欢它。不幸的是，如果没有企业版，IPython+Notebook+(现在的Jupyter)是不可用的。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|491360|我想在教室里使用Jupyter笔记本，所以我换成了Canopy。安装我们需要的所有包似乎很容易。诚然，我们还没有对它们进行全部测试。|491361|entityMap|0|LINK|mutability|MUTABLE|url|https://en.wikipedia.org/wiki/IPython#Notebook|1|https://en.wikipedia.org/wiki/IPython#Project_Jupyter^0|11|G|0|1M|7|1|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]]|C|$]]|$1|D|3|E|5|6|7|W|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

I used Anaconda for years and liked it quite a bit. Unfortunately, <a href="https://en.wikipedia.org/wiki/IPython#Notebook" rel="nofollow noreferrer">IPython Notebook</a> (now <a href="https://en.wikipedia.org/wiki/IPython#Project_Jupyter" rel="nofollow noreferrer">Jupyter</a>) is unavailable without the enterprise edition. 

I want to use Jupyter notebooks in the classroom, so I switched to Canopy. It seems easy enough to install all of the packages we need. Admittedly, we haven't tested them all.

What are the relative merits / downsides of various Python bundles (EPD / Anaconda) vs. a manual install?

I have installed EPD academic, and I have no issues with it. It provides more packages that I think I will ever need, and it is very easy to update using enpkg enstaller. The EPD academic licence requires yearly renewal however and the free version does not do updates as easily.

At the moment I really only use a handful of packages such as <a href="https://en.wikipedia.org/wiki/Pandas_%28software%29">Pandas</a>, <a href="http://en.wikipedia.org/wiki/NumPy">NumPy</a>, <a href="http://en.wikipedia.org/wiki/SciPy">SciPy</a>, <a href="http://en.wikipedia.org/wiki/Matplotlib">matplotlib</a>, <a href="http://en.wikipedia.org/wiki/IPython">IPython</a>, <a href="http://statsmodels.sourceforge.net/">Statsmodels</a> and their respective dependencies. 

For such limited use am I better off with manual install and <code>pip install --upgrade 'package'</code> or do the bundles offer anything over and above this?

Anaconda vs. EPD Enthought vs. manual installation of Python

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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与手动安装相比，各种Python包(EPD / Anaconda)的优缺点是什么？我已经安装了EPD学术版，我对它没有任何问题。它提供了更多的包，我认为我将永远需要，它是非常容易更新使用enpkg enstaller。然而，环保署的学术执照需要每年续期，而免费版本的更新则不那么容易。目前，我实际上只使用了少数几个包，如...

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