These can be thought of, respectively, as three-dimensional and four-dimensional generalizations of the (one-dimensional) Series and (two-dimensional) DataFrame structures. Pandas has a few other fundamental data structures that we have not yet discussed, namely the pd.Panel and pd.Panel4D objects. In this section, we'll explore the direct creation of MultiIndex objects, considerations when indexing, slicing, and computing statistics across multiply indexed data, and useful routines for converting between simple and hierarchically indexed representations of your data.
In this way, higher-dimensional data can be compactly represented within the familiar one-dimensional Series and two-dimensional DataFrame objects. While Pandas does provide Panel and Panel4D objects that natively handle three-dimensional and four-dimensional data (see Aside: Panel Data), a far more common pattern in practice is to make use of hierarchical indexing (also known as multi-indexing) to incorporate multiple index levels within a single index. Often it is useful to go beyond this and store higher-dimensional data–that is, data indexed by more than one or two keys. Up to this point we've been focused primarily on one-dimensional and two-dimensional data, stored in Pandas Series and DataFrame objects, respectively.
