I am just using Excel data analysis workers. One day, my friend and I made an appointment for dinner at night, five minutes away from work, my boss suddenly Q:
Boss: Today you add classes
I: Haoyahaoya
boss: I have several Excel, you need to put them together into one table
I: Haoyahaoya
boss: Give! You can figure it out!
With disturbed feelings I opened a mysterious archive:
912 CSV tables, each table a total of 370, ranging from about 360 lines
so much! I tried Power Query, computer bluntly Xiecai, which if pure have to manually copy the company can not sleep. I am paralyzed on the station: "I was too afraid to spend the night and 912 table today."
Friends listening to the ins and outs but laughed: "trivial, tonight eat this meal really set up, look at me!"
I looked dubious friend to open a black window, brush brush to knock a few lines of code, a good form merger completed very quickly!
Also on the screen even jumped out Duration:
Smoothly from work! Before I always felt that Excel can do a lot of things, their own motivation to learn is not particularly strong, a lot of time muddling along, and see the code a little daunting, it looks as though it is difficult sub-sub, but did not expect so easy.
I could not stand it: Peng brother, you will of this code, how so powerful, better use than Excel ah?
Dapeng mysterious smile: Python can be stronger, much more than you see.
The power of Python
No.1 high efficiency, reusable
Just processing efficiency of the table you see, the more powerful is if there is a similar task, and we only need to change the working directory, this will be the code can be used directly, can be described once and for all.
In addition to the combined form of such requirements, the batch is not showing also been bothering you? Think about how you use Excel to do data analysis:
Every step must figure mouse click from the cleaning of data to sort out, tedious and error-prone, and just a few lines of code using Python can easily showing:
When you are faced with high repetitive work, only need minor changes, or the introduction of circulation, no longer have to point the mouse hand cramp.
我有点心动：好像是比Excel方便多了，会用Python肯定能大大提高工作效率。
Python的强大之处
No.2 功能丰富，涵盖完整的数据工作流
就在我在心里为大鹏的表演喊“666”的同时，又滔滔不绝地讲了起来：你别看我前面只提到了使用Python整整表格出出图，人家可是著名的“胶水语言”。
“胶水语言”是什么？我问道。
朋友解释道：Python可以利用MySQLdb库连接数据库，可以利用pandas和matplotlib进行清洗和分析，可以利用pyecharts进行交互可视化，可以利用numpy和sklearn进行建模，甚至可以利用pyinstaller打包工作流交给同事，共同提效……
而且这些库的丰富程度，可以说是超出你的想象，以python可视化必知基本库matplotlib为例，光是他的官方gallery就有26个大类527个样式，数量上就碾压了市面上大部分同功能软件。
Python可视化类工具会有针对图表样式进行调整的代码，也可以交互，几行代码，省时省力，分分钟关机下班。
比较一下Seaborn的图表库和Excel的图表库，感受差距：
这就有点惊讶到我了：这效率和酷炫程度和Excel根本不是一个层级的。这么游刃有余的本事，不可谓不吸引人啊！会用Python肯定能做更多的事情，让老板刮目相看。
Python的强大之处
No.3 时代所趋，易学好用
我随手找了一点资料：Python官方在今年2月做了一份报告，从官方的角度说明了python的使用状况和受欢迎程度。
该调查由 Python 软件基金会与 JetBrains 一起发起，有来自 150 多个国家的超过两万名开发人员参与。小编整理一套Python资料和PDF，有需要Python学习资料可以加学习群：631441315 ，反正闲着也是闲着呢，不如学点东西啦~~
从官方喜出望外的报告中，我发现python受到大部分人的欢迎，是用户手中的香饽饽：
在python的用途上，大家使用python最常用的场景是数据分析，并且相比2017年，2018年的涨幅也是相比最高的，相关的机器学习场景涨幅也有7%。
python语言的这种火热程度也是不难理解了。看来，使用Python进行数据分析是时代的趋势。