How to analyze pivot_table PivotTable
This article will explain in detail how to analyze the pivot_table PivotTable. The content of the article is of high quality, so the editor will share it for you as a reference. I hope you will have a certain understanding of the relevant knowledge after reading this article.
PivotTable is a powerful tool for classifying and summarizing data and statistical analysis. By setting new row label index and column label columns, specifying the numerical values to be statistically analyzed, specifying the statistical aggregation function aggfunc, etc., PivotTable can be used to analyze the original data table from multiple perspectives and reshape it in different ways, so it is called PivotTable.
In the Pandas of Python, you can use the groupby method or pivot_table function to complete the classification and summary to achieve the function of PivotTable. Groupby is first grouped, and then the aggregate function is selected to generate a PivotTable. Pivot_table generates PivotTable directly by setting parameters such as index,columns,values,aggfunc.
First, PivotTable in Excel
The PivotTable in Excel can set rows (index), columns (columns), values (values), and select aggregate functions through value field settings. The graphical interface is relatively simple to operate, but not flexible and powerful enough.
Second, pivot_table PivotTable
Compared with the PivotTable in Excel, using the pivot_table function of pandas to implement the PivotTable will be very flexible and powerful.
Construct dataframe data
1the parameter of index
2Parameters of columns
3Parameters of values
4the parameter of dint aggfunc
5 Overview of the parameters of the initialtable
Third, groupby data packet function
Using the grouping function of groupby method combined with aggregate function can also achieve PivotTable effect, which is also a very common method in data analysis. The demonstration operation is as follows.
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