caikit.interfaces.ts.data_model.backends.pandas_backends

Core data model backends backed by pandas

Attributes

`log`
`error`

Classes

`PandasMultiTimeSeriesBackend`	Abstract base class for all backends of the central MultiTimeSeries data model
`PandasTimeSeriesBackend`	The PandasTimeSeriesBackend is responsible for managing the standard
`PandasValueSequenceBackend`	Backend for ValueSequence backed by a set of columns in a Pandas
`PandasPeriodicTimeSequenceBackend`	Backend for PeriodicTimeSequence backed by a Pandas Time Span
`PandasPointTimeSequenceBackend`	Backend for PointTimeSequence backed by a Pandas Series
`PandasTimePointBackend`	Backend for time point data held by Pandas

Module Contents

caikit.interfaces.ts.data_model.backends.pandas_backends.log[source]

caikit.interfaces.ts.data_model.backends.pandas_backends.error

class caikit.interfaces.ts.data_model.backends.pandas_backends.PandasMultiTimeSeriesBackend(data_frame: pandas.DataFrame, key_column: Iterable[str] | str, timestamp_column: str | None = None, value_columns: Iterable[str] | None = None, ids: Iterable[int] | Iterable[str] | None = None, producer_id: Tuple[str, str] | caikit.core.data_model.ProducerId | None = None)[source]

Bases: caikit.interfaces.ts.data_model.backends.base.MultiTimeSeriesBackendBase

Abstract base class for all backends of the central MultiTimeSeries data model type

as_pandas() → Tuple[pandas.DataFrame, Iterable[str], str, Iterable[str]][source]

All backends must implement the ability to coerce their underlying data into a pandas DataFrame and provide the pointers to the timeseries source and value source(s)

Returns:

df: pd.DataFrame: The data frame itself
key_source: Iterable[str]: the names of the columns holding key values
timestamp_source: str: The column name (or None) indicating where the timestamp sequence can be found
value_source: Iterable[str]: The names of the columns holding value sequences

_df

_key_column

_timestamp_column = None

_value_columns

_ids = []

_producer_id

_timeseries = None

_key_columns

get_attribute(data_model_class: Type[caikit.interfaces.ts.data_model.timeseries.TimeSeries], name: str) → Any[source]

A data model backend must implement this in order to provide the frontend view the functionality needed to lazily extract data.

Args:

data_model_class (Type[DataBase]): The frontend data model class: that is accessing this attribute

name (str): The name of the attribute to access

Returns:

value: Union[Any, OneofFieldVal]: The extracted attribute value or a OneofFieldVal that wraps the field val with an indicator about the oneof field that is set.

class caikit.interfaces.ts.data_model.backends.pandas_backends.PandasTimeSeriesBackend(data_frame: pandas.DataFrame, timestamp_column: str = None, value_columns: Iterable[str] | None = None, ids: Iterable[int] | Iterable[str] | None = None)[source]

Bases: caikit.interfaces.ts.data_model.backends.base.TimeSeriesBackendBase

The PandasTimeSeriesBackend is responsible for managing the standard in-memory representation of a TimeSeries

_df

_timestamp_column = None

_value_columns

_ids = []

get_attribute(data_model_class: Type[caikit.interfaces.ts.data_model.SingleTimeSeries], name: str, external_df: pandas.DataFrame = None) → Any[source]: When fetching a data attribute from the timeseries, this aliases to the appropriate set of backend wrappers for the various fields.

as_pandas() → Tuple[pandas.DataFrame, str, Iterable[str]][source]: Return the underlying data frame

class caikit.interfaces.ts.data_model.backends.pandas_backends.PandasValueSequenceBackend(data_frame: pandas.Series, col_name: str)[source]

Bases: caikit.interfaces.ts.data_model.backends.base.UncachedBackendMixin, caikit.interfaces.ts.data_model.backends.base.StrictFieldBackendMixin

Backend for ValueSequence backed by a set of columns in a Pandas DataFrame

static _serialize_any(any_val)[source]

_TIMESTAMP_DTYPE

_DEFAULT_VECTOR_TYPES

_df

_col_name

_dtype

_converter

get_attribute(data_model_class: Type[caikit.interfaces.ts.data_model.time_types.ValueSequence], name: str) → Any[source]: Get the known attributes from the underlying DataFrame columns

class caikit.interfaces.ts.data_model.backends.pandas_backends.PandasPeriodicTimeSequenceBackend(time_sequence)[source]

Bases: caikit.interfaces.ts.data_model.backends.base.UncachedBackendMixin, caikit.interfaces.ts.data_model.backends.base.StrictFieldBackendMixin

Backend for PeriodicTimeSequence backed by a Pandas Time Span

_is_range_index

get_attribute(data_model_class: Type[caikit.interfaces.ts.data_model.time_types.PeriodicTimeSequence], name: str) → Any[source]: Get the known attributes from the backend data

class caikit.interfaces.ts.data_model.backends.pandas_backends.PandasPointTimeSequenceBackend(time_sequence: pandas.Series)[source]

Bases: caikit.interfaces.ts.data_model.backends.base.UncachedBackendMixin, caikit.interfaces.ts.data_model.backends.base.StrictFieldBackendMixin

Backend for PointTimeSequence backed by a Pandas Series

_time_sequence

get_attribute(data_model_class: Type[caikit.interfaces.ts.data_model.time_types.PointTimeSequence], name: str) → Any[source]: Get the known attributes from the backend data

class caikit.interfaces.ts.data_model.backends.pandas_backends.PandasTimePointBackend(point_data: Any)[source]

Bases: caikit.interfaces.ts.data_model.backends.base.UncachedBackendMixin, caikit.interfaces.ts.data_model.backends.base.StrictFieldBackendMixin

Backend for time point data held by Pandas

_point_data

get_attribute(data_model_class: Type[caikit.interfaces.ts.data_model.time_types.TimePoint], name: str) → Any[source]: Get the appropriate fields based on the data type of the point