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Python how to use Jupyter to do data analysis, in view of this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

After loading a Jupyter plug-in, you can do data analysis without writing code and help you generate the corresponding code?

Yes, just load this gadget called Mito and do data analysis with Python, which is as simple as using Excel: it's faster than Excel, and you don't have to search for Python tutorials everywhere. Easy to use, such as Excel, faster and more comprehensive Mito is an editable spreadsheet plug-in for Jupyter notebook that generates relevant Python code when editing a .csv table (with format conversion function). △ Mito, an acronym for mitochondrial Mitochondria, specifically, the emergence of Mito seems to combine the power of Python with the ease of use of Excel. As long as you master the use of Excel, you can use the data analysis capabilities of Python, and you can "pack and take away" the written code. It makes up for several shortcomings of Excel in data analysis:

Excel cannot do big data analysis (large data sets are not handled well)

Excel runs slowly

Excel cannot easily create repeatable processes

At the same time, it is simpler and more intuitive than SQL and Python. After all, for basic 0 beginners, these professional tools will take at least a few years before they can be fully used. According to Mito internal test users, the plug-in makes them 10 times more efficient in doing data analysis with Python, because users can write Excel formulas directly in Mito, such as = SUM (A1100). So how does Mito translate Excel logic into Python code? The authors wrote a program called Transpiler, which is somewhat similar to the function of a compiler, using abstract syntax trees (AST), parsing Excel source code, and converting it into Python source code.

Compared to using professional software such as Alteryx ($5000 / month) for data analysis, the Python code generated by Mito can be modified as needed, which is a little more flexible. Currently, Mito uses Amazon Cloud platform (AWS) to store users' data, and each user has a separate account. Of course, users can also choose to save the data locally. Automatically generate Python code to analyze the relationship between "average household income" and "number of train stations allowed to ship" in American states as an example. First, upload data on "average household income" and "number of train stations allowed to ship". The format of the data processing is .csv, of course, you can also enter an Excel file and convert it into two .csv files using Mito. Then, to merge the two datasets together, you only need to use the mouse to check the corresponding functions and select the relevant data columns. Bang! The code is generated.

Then, the PivotTable is made, and after the grouping is completed, the aggregation method is switched by the aggregate function. It also includes data filtering, which can also generate the relevant code immediately. Including ascending and descending sorting function, fast and concise. Then is the relevant data statistics, analysis of the results, the process is intuitive. The method of saving the analysis file is also very simple, the file is written in Python, rather than using the more difficult to understand VBA. It is also very easy to repeat the above steps. Mito comes with the "repeat saved Analysis steps" function, and other data can be analyzed in the same way with a single click. It's really much easier than writing code line by line. The answer to the question about how Python uses Jupyter to do data analysis is shared here. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel to learn more about it.

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