Internals#

This section will provide a look into some of pandas internals. It’s primarily intended for developers of pandas itself.

Indexing#

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 O(1) lookups.

  • MultiIndex: the standard hierarchical index object

  • DatetimeIndex: An Index object with Timestamp boxed elements (impl are the int64 values)

  • TimedeltaIndex: An Index object with Timedelta boxed elements (impl are the in64 values)

  • 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

  • period_range(): fixed frequency date range generated from a time rule or DateOffset. An ndarray of Period objects, representing timespans

Warning

Custom Index subclasses are not supported, custom behavior should be implemented using the ExtensionArray interface instead.

MultiIndex#

Internally, the MultiIndex consists of a few things: the levels, the integer codes, and the level names:

In [1]: index = pd.MultiIndex.from_product(
   ...:     [range(3), ["one", "two"]], names=["first", "second"]
   ...: )
   ...: 

In [2]: index
Out[2]: 
MultiIndex([(0, 'one'),
            (0, 'two'),
            (1, 'one'),
            (1, 'two'),
            (2, 'one'),
            (2, 'two')],
           names=['first', 'second'])

In [3]: index.levels
Out[3]: FrozenList([[0, 1, 2], ['one', 'two']])

In [4]: index.codes
Out[4]: FrozenList([[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

In [5]: index.names
Out[5]: 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 constructors from_tuples() and from_arrays() ensure that this is true, but if you compute the levels and codes yourself, please be careful.

Values#

pandas extends NumPy’s type system with custom types, like Categorical or datetimes with a timezone, so we have multiple notions of “values”. For 1-D containers (Index classes and Series) we have the following convention:

  • cls._values refers is the “best possible” array. This could be an ndarray or ExtensionArray.

So, for example, Series[category]._values is a Categorical.

Subclassing pandas data structures#

This section has been moved to Subclassing pandas data structures.