If you'd like to read more about the Panel and Panel4D structures, see the references listed in Further Resources. It plots a line chart of the series values by default but you can specify the type of chart to plot using the kind parameter. We won't cover these panel structures further in this text, as I've found in the majority of cases that multi-indexing is a more useful and conceptually simpler representation for higher-dimensional data.Īdditionally, panel data is fundamentally a dense data representation, while multi-indexing is fundamentally a sparse data representation.Īs the number of dimensions increases, the dense representation can become very inefficient for the majority of real-world datasets.įor the occasional specialized application, however, these structures can be useful. To plot a pandas series, you can use the pandas series plot () function. In particular, the ix, loc, and iloc indexers discussed in Data Indexing and Selection extend readily to these higher-dimensional structures. Once you are familiar with indexing and manipulation of data in a Series and DataFrame, Panel and Panel4D are relatively straightforward to use. 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. Similarly, in the Series constructor: Parameters: data: array-like, Iterable, dict, or scalar value. If a dict contains Series which have an index defined, it is aligned by its index. operations directly with the unstack and pivot methods respectively. If data is a dict, column order follows insertion-order. Recipes for Scientific Computing, Time Series Analysis and Data. 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. Dict can contain Series, arrays, constants, dataclass or list-like objects. 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.
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