How to use Pandas Multi-level Index
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
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