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萧箫 发自 凹非寺
量子位 报道 | QbitAI
加载一个Jupyter插件后，无需写代码就能做数据分析，还帮你生成相应代码？
没错，只需要加载这个名为Mito的小工具包，用Python做数据分析，变得和用Excel一样简单：
运行速度比Excel更快，也不需要到处搜各种Python教程了。
好用如Excel，更快更全面
Mito是Jupyter notebook的一个可编辑电子表格插件，在编辑.csv表格（带格式转换功能）时，就能生成相关Python代码。
△Mito，线粒体Mitochondria的缩写
具体来说，Mito的出现，像是将Python的强大功能、和Excel的易用性进行了结合。
只需要掌握Excel的用法，就能使用Python的数据分析功能，还能将写出来的代码“打包带走”。
它弥补了Excel在数据分析上的几个缺陷：
-
Excel无法做大数据分析（大型数据集处理得不好）
-
Excel运行缓慢
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Excel无法轻松创建可重复流程
同时，又比SQL和Python更简单、直观。毕竟这些专业工具对于0基础初学者来说，需要至少几年时间，才能完全上手。
据Mito内测用户表示，这款插件让他们用Python做数据分析的效率提升了10倍，因为用户可以直接在Mito里编写Excel公式，如=SUM(A1, 100)。
那么，Mito是怎么做到将Excel逻辑转换成Python代码的呢？
作者们编写了一种名为Transpiler的程序，有点类似于编译器的功能，采用抽象语法树（AST），解析Excel源代码，并转换成Python的源代码。
相比于采用专业软件如Alteryx（需要5000美元/月）进行数据分析，Mito所生成的Python代码可以根据需要自行修改，灵活性更高一点。
目前，Mito采用亚马逊云平台 （AWS）保存用户的相关数据，每个用户拥有一个独立账户。
当然，用户也可以选择将数据保存在本地。
自动生成Python代码
以分析美国各州的“家庭平均收入”和“允许托运的火车站数量”这两个数据的关系为例。
首先，上传“家庭平均收入”和“允许托运的火车站数量”两份数据。
数据处理的格式是.csv，当然也可以输入Excel文件，并用Mito转成两份.csv文件。
然后，将这两份数据集合并在一起，只需要用鼠标勾选对应功能、选中相关数据列就行。
啪！代码就生成好了。
然后，是做数据透视表，在完成分组后，采用聚合（aggregate）功能来切换聚合方法。
还包括数据过滤功能，同样立刻就能生成相关代码。
包含升降序排序功能，快速简洁。
然后就是相关数据统计、分析出结果了，流程直观。
保存分析文件的方法也很简单，文件是以Python编写的，而不是用比较难懂的VBA。
要想重复上面的步骤的话，也非常容易，Mito自带“重复已保存分析步骤”功能，一键就能用同样的方法分析其他数据。
确实要比一行行编写代码简单多了。
关于Mito
那么，Mito的作者们，为什么要搞这个软件？
因为他们发现，所谓的“几天上手Python数据分析”，其实根本没有那么容易……
初学者要想用Python搞数据分析，就得不停地查看各种文档、和求助于StackOverflow。
要想真正快速用Python分析数据，最后还得自己编写软件。
三位作者Aaron Diamond-Reivich、Jake Diamond-Reivich和Nate Rush都来自宾大，在学校期间，他们学习了计算机科学、统计学和商业分析相关的课程。
也正是在搞数据分析的时候，他们萌生了想要制作Mito的想法。
作者表示，软件目前还没有开源，因为他们还在思考，如何支持维护这个项目，并转到开源路径上来。
不过，它现在已经可以使用了。
感兴趣的小伙伴们，可以上手试试了~
项目主页：
https://trymito.io/launch
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