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Editor to share with you how to use the Pandas multi-level index, I believe most people do not know much about it, so share this article for your reference, I hope you can learn a lot after reading this article, let's go to know it!

The name of the Pandas library comes from the abbreviations of the initials of three of the main data structures:

Panel,Dataframe,Series .

Series represents one-dimensional data, Dataframe represents two-dimensional data, and Panel represents three-dimensional data.

But in fact, when the data is higher than two-dimensional, we usually use Dataframe that contains multi-level indexes instead of using Panel.

The reason is that using multi-level indexes to display data is more intuitive, manipulating data is more flexible, and can represent 3D, 4D or even any dimensional data.

First, the creation of multi-level index

1, specify the multidimensional list as the columns

2. Explicitly generate a multi-level index using the method in pd.MultiIndex

You can use methods such as from_tuples in pd.MultiIndex to generate multi-level indexes.

3. Convert a normal column to a multi-level index using the set_index method

This method can only generate multi-level row indexes.

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

Second, the value of multi-level index

Multi-level index Series or multi-level DataFrame supports direct square bracket values, loc values, and pd.IndexSlice slice values.

1, the value of multi-level Series

2, the value of multi-level DataFrame

Third, multi-level index related operations

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

1Stack and unstack

2Jet setbacks index and reset_index

3. Specify the relevant methods of level

The above is all the content of the article "how to use Pandas Multi-level Index". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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