PanelPdMultiIndex

class PanelPdMultiIndex(is_univariate=None, is_equally_spaced=None, is_equal_length=None, is_empty=None, is_one_series=None, has_nans=None, n_instances=None, n_features=None, feature_names=None, dtypekind_dfip=None, feature_kind=None)[source]

Data type: MultiIndex-ed pd.DataFrame specification of panel of time series.

Name: "pd-multiindex"

Short description:

a pandas.DataFrame, with row multi-index (instances, time), cols = variables

Long description:

The "pd-multiindex" mtype is a concrete specification that implements the Panel scitype, i.e., the abstract type of a collection of time series.

An object obj: pd.DataFrame follows the specification iff:

structure convention: obj.index must be a pair multi-index of type (Index, t), where t is one of Int64Index, RangeIndex, DatetimeIndex, PeriodIndex and monotonic. obj.index must have two levels (can be named or not).
instance index: the first element of pairs in obj.index (0-th level value) is interpreted as an instance index, we call it “instance index” below.
instances: rows with the same “instance index” index value correspond to the same instance; rows with different “instance index” values correspond to different instances.
time index: the second element of pairs in obj.index (1-st level value) is interpreted as a time index, we call it “time index” below.
time points: rows of obj with the same “time index” value correspond correspond to the same time point; rows of obj with different “time index” index correspond to the different time points.
variables: columns of obj correspond to different variables
variable names: column names obj.columns

Capabilities:

can represent panels of multivariate series
can represent unequally spaced series
can represent panels of unequally supported series
cannot represent panels of series with different sets of variables
can represent missing values

Parameters:

is_univariate: bool: True iff table has one variable
is_equally_spacedbool: True iff series index is equally spaced
is_equal_length: bool: True iff all series in panel are of equal length
is_empty: bool: True iff table has no variables or no instances
is_one_series: bool: True iff there is only one series in the panel of time series
has_nans: bool: True iff the table contains NaN values
n_instances: int: number of instances in the panel of time series
n_features: int: number of variables in table
feature_names: list of int or object: names of variables in table
dtypekind_dfip: list of DtypeKind enum: list of DtypeKind enum values for each feature in the panel, following the data frame interface protocol
feature_kind: list of str: list of feature kind strings for each feature in the panel, coerced to FLOAT or CATEGORICAL type

Methods

`__call__`(obj[, return_metadata, var_name, ...])	Check if obj is of this data type.
`check`(obj[, return_metadata, var_name, ...])	Check if obj is of this data type.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`get`(key[, default])	Get attribute by key.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the skbase object.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.