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Using Pandas, what are the reasons to use a Panel versus a MultiIndex DataFrame?
I have personally found significant difference between the two in the ease of accessing different dimensions/levels, but that may just be my being more familiar with the interface for one versus the other. I assume there are more substantive differences, however.
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The MultiIndex object is the hierarchical analogue of the standard Index object which typically stores the axis labels in pandas objects. You can think of MultiIndex as an array of tuples where each tuple is unique. A MultiIndex can be created from a list of arrays (using MultiIndex.
A panel is a 3D container of data. The term Panel data is derived from econometrics and is partially responsible for the name pandas − pan(el)-da(ta)-s. The names for the 3 axes are intended to give some semantic meaning to describing operations involving panel data.
A MultiIndex, also known as a multi-level index or hierarchical index, allows you to have multiple columns acting as a row identifier, while having each index column related to another through a parent/child relationship.
DeprecationWarning: Panel is deprecated and will be removed in a future version. The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel. to_frame() method. Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/.
In my practice, the strongest, easiest-to-see difference is that a Panel needs to be homogeneous in every dimension. If you look at a Panel as a stack of Dataframes, you cannot create it by stacking Dataframes of different sizes or with different indexes/columns. You can indeed handle more non-homogeneous type of data with multiindex.
So the first choice has to be made based on how your data is to be organized.
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Panel has been deprecated in pandas v0.20.1 (May 5, 2017) and will be removed in a future version. It is recommended to represent 3-D data with a MultiIndex on a DataFrame via the to_frame() or with the xarray package.
References:
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