Python's learning approach to data analysis

python data analysis requirements are not software development requirements , indeed, for a tool, different purposes of the user, the required skills are not the same, such as knife This tool, the butcher used it to kill pigs, the chef used it is cut vegetables, military use it is defend, the guests use it is cut steak, Everyone uses different ways, there are specific requirements for the method of mastering the knife.

python data analysis, like Excel for data analysis , is from understanding how Python is used, how to process data in it, how to filter data, how to analyze it, and how to display it graphically. Python is just a tool, the key or the way to think about problems. The purpose of our learning Python is not to write Web sites, write applications, write crawlers, but to analyze data, so our scope shrinks to what Python is and how it uses basic functionality, and how data is analyzed in two big parts.

python data analysis, step three :

Step one, basically use NumPy library to do statistical calculation. This is like the function of statistical summary functions in the "Data" menu in Excel.

Step Two: Learn to use pandas, its birth (2009 Open source) is for financial data analysis. Pandas is a tool developed on the basis of numpy. Make statistics more convenient.

Step three, Matplotlib is python in the data analysis display with the comparison of the main library, in addition, there are many graphics library is based on matplotlib development, display effect will be more friendly and convenient. Such asVispy,bokeh,Seaborn,Pyga, Folium and networkx , the simple use of these 7 graphical tools libraries can be found in the http://python.jobbole.com/84218/introduction. However, I personally think it is not necessarily to do data display, or that sentence, to see what each user needs.

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Reference pages

Http://www.cnblogs.com/nxld/p/6058998.html

http://python.jobbole.com/84218/

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Python's learning approach to data analysis