This section will provide a look into some of pandas internals. It’s primarily intended for developers of pandas itself.
In pandas there are a few objects implemented which can serve as valid containers for the axis labels:
Index: the generic “ordered set” object, an ndarray of object dtype assuming nothing about its contents. The labels must be hashable (and likely immutable) and unique. Populates a dict of label to location in Cython to do
MultiIndex: the standard hierarchical index object
PeriodIndex: An Index object with Period elements
There are functions that make the creation of a regular index easy:
date_range(): fixed frequency date range generated from a time rule or DateOffset. An ndarray of Python datetime objects
Index subclasses are not supported, custom behavior should be implemented using the
ExtensionArray interface instead.
MultiIndex consists of a few things: the levels, the
integer codes, and the level names:
In : index = pd.MultiIndex.from_product( ...: [range(3), ["one", "two"]], names=["first", "second"] ...: ) ...: In : index Out: MultiIndex([(0, 'one'), (0, 'two'), (1, 'one'), (1, 'two'), (2, 'one'), (2, 'two')], names=['first', 'second']) In : index.levels Out: FrozenList([[0, 1, 2], ['one', 'two']]) In : index.codes Out: FrozenList([[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]]) In : index.names Out: FrozenList(['first', 'second'])
You can probably guess that the codes determine which unique element is
identified with that location at each layer of the index. It’s important to
note that sortedness is determined solely from the integer codes and does
not check (or care) whether the levels themselves are sorted. Fortunately, the
that this is true, but if you compute the levels and codes yourself, please be careful.
pandas extends NumPy’s type system with custom types, like
datetimes with a timezone, so we have multiple notions of “values”. For 1-D
Index classes and
Series) we have the following convention:
cls._valuesrefers is the “best possible” array. This could be an
So, for example,
Series[category]._values is a
Subclassing pandas data structures#
This section has been moved to Subclassing pandas data structures.