I’m going to learn how to use Pandas to do multilevel indexing. I’m sure it will be difficult to learn how to do it. The Python tutorial has been published in the past, but here is another one!

The library’s name is derived from the acronyms for the beginning letters of three major data structures: Panel,Dataframe, and Series. Where Series represents one-dimensional data, Dataframe represents two-dimensional data, and Panel represents three-dimensional data. When data is higher than two dimensions, Panel is generally not used. Why? If you don’t use Panel, what should you do?

In fact, when data is larger than two dimensions, we typically use a Dataframe with a multilevel index rather than a Panel. The reason is that using multilevel indexes makes it more intuitive to present data, more flexible to manipulate data, and can represent data in 3, 4, or any dimension. So how do you do that? Below we teach you some methods from the creation, value and operation of multi – level index.

Create a multilevel index

1. Specify multidimensional lists as columns



2. Explicitly generate a multilevel index using the method in pd.MultiIndex

Multilevel indexes can be generated using methods such as from_tuples in pd.multiindex.



3. Use the set_index method to turn an ordinary column into a multilevel index

This approach can only generate multilevel row indexes.







4. Methods such as Groupby and Pivot_table can also generate results with multi-level indexes





2. The value of the multi-level index

Series or DataFrame supports square brackets, LOC, and pd.IndexSlice.

1. The value of multi-tier Series









2. Multilevel DataFrame values











3. Multi-level index related operations

Multi-level index operations include stack and unstack,set_index and reset_index, and methods for specifying level.

Stack and unstack






2. Set_index and reset_index





3, specify the related methods of level








Partners those places are not clear can leave a message oh!