caikit.interfaces.ts.data_model._single_timeseries
==================================================

.. py:module:: caikit.interfaces.ts.data_model._single_timeseries

.. autoapi-nested-parse::

   The core data model object for a TimeSeries



Attributes
----------

.. autoapisummary::

   caikit.interfaces.ts.data_model._single_timeseries.log
   caikit.interfaces.ts.data_model._single_timeseries.error


Classes
-------

.. autoapisummary::

   caikit.interfaces.ts.data_model._single_timeseries.SingleTimeSeries


Module Contents
---------------

.. py:data:: log

.. py:data:: error

.. py:class:: SingleTimeSeries(*args, **kwargs)

   Bases: :py:obj:`caikit.core.DataObjectBase`


   The TimeSeries object is the central data container for the library.
   At present it wraps either a pandas.DataFrame, or pyspark.sql.DataFrame to bind
   into the caikit data model.


   .. py:class:: StringIDSequence

      Bases: :py:obj:`caikit.core.DataObjectBase`


      Nested value sequence of strings


      .. py:attribute:: values
         :type:  py_to_proto.dataclass_to_proto.Annotated[List[str], FieldNumber(1)]



   .. py:class:: IntIDSequence

      Bases: :py:obj:`caikit.core.DataObjectBase`


      Nested value sequence of ints


      .. py:attribute:: values
         :type:  py_to_proto.dataclass_to_proto.Annotated[List[int], FieldNumber(1)]



   .. py:attribute:: time_sequence
      :type:  Union[py_to_proto.dataclass_to_proto.Annotated[caikit.interfaces.ts.data_model.time_types.PeriodicTimeSequence, OneofField('time_period'), FieldNumber(10)], py_to_proto.dataclass_to_proto.Annotated[caikit.interfaces.ts.data_model.time_types.PointTimeSequence, OneofField('time_points'), FieldNumber(20)]]


   .. py:attribute:: values
      :type:  py_to_proto.dataclass_to_proto.Annotated[List[caikit.interfaces.ts.data_model.time_types.ValueSequence], FieldNumber(1)]


   .. py:attribute:: timestamp_label
      :type:  py_to_proto.dataclass_to_proto.Annotated[str, FieldNumber(2)]


   .. py:attribute:: value_labels
      :type:  py_to_proto.dataclass_to_proto.Annotated[List[str], FieldNumber(3)]


   .. py:attribute:: ids
      :type:  Union[py_to_proto.dataclass_to_proto.Annotated[SingleTimeSeries.IntIDSequence, OneofField('id_int'), FieldNumber(30)], py_to_proto.dataclass_to_proto.Annotated[SingleTimeSeries.StringIDSequence, OneofField('id_str'), FieldNumber(40)]]


   .. py:attribute:: _DEFAULT_TS_COL
      :value: 'timestamp'



   .. py:method:: _get_pd_df() -> Tuple[pandas.DataFrame, str, Iterable[str]]

      Convert the data to a pandas DataFrame, efficiently if possible



   .. py:method:: __len__() -> int

      Return the length of the single time series object.

      Returns:
          int: Length



   .. py:method:: __eq__(other: SingleTimeSeries) -> bool

      Equivalence operator for SingleTimeSeries objects.

      Performs ordering of data based on timestamp_label prior to checking for equivalence. Relies
      on underlying pandas equivalence testing function `pd.testing.assert_frame_equal`.

      Args:
          other (SingleTimeSeries): SingleTimeSeries to test against.

      Returns:
          bool: True if the SingleTimeSeries are equivalent.



   .. py:method:: _as_pandas_ops(adf, include_timestamps: Union[None, bool] = False)

      operate on pandas-like object instead of strictly pandas



   .. py:method:: as_pandas(include_timestamps: Optional[bool] = None) -> pandas.DataFrame

      Get the view of this timeseries as a pandas DataFrame

      Args:
          include_timestamps (bool, optional): Control the addition or removal of
          timestamps. True will include timestamps, generating if needed, while False will
          remove timestamps. Use None to returned what is available, leaving unchanged.
          Defaults to None.

      Returns:
          pd.DataFrame: The view of the data as a pandas DataFrame



   .. py:method:: as_spark(include_timestamps: Optional[bool] = None) -> caikit.interfaces.ts.data_model.toolkit.optional_dependencies.pyspark.sql.DataFrame

      Get the view of this timeseries as a spark DataFrame

      Args:
          include_timestamps (bool, optional): Control the addition or removal of
          timestamps. True will include timestamps, generating if needed, while False will
          remove timestamps. Use None to returned what is available, leaving unchanged.
          Defaults to None.

      Returns:
          pyspark.sql.DataFrame: The view of the data as a spark DataFrame