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Transformer

PandasTransformAdaptor

API referenceSource
MultivariateUnequal-length series

Adapt pandas transformations to sktime interface.

In transform, executes pd.DataFrame method of name method on data, optionally with keywords arguments passed, via kwargs hyper-parameter. The apply_to parameter controls what the data is upon which method is called: “call” = for X seen in transform, “all”/”all_subset” = all data seen so far. See below for details.

For hierarchical series, operation is applied by instance.

Quickstart

python
from sktime.transformations.adapt import PandasTransformAdaptor

estimator = PandasTransformAdaptor(method, kwargs=None, apply_to='call')

Tags

Capabilities

  • Multivariate
  • Unequal-length series
  • Inverse transform: Not supported
  • Missing values: Not supported
  • Removes missing values: Not supported
  • Equalizes series length: Not supported

Properties

Input typescitype:transform-input
Series
Output typescitype:transform-output
Series
Label typescitype:transform-labels
None
Fit is emptyfit_is_empty
No
Keeps the time indextransform-returns-same-time-index
No
Requires Xrequires_X
Yes
Requires yrequires_y
No
X and y need the same indexX-y-must-have-same-index
No

Parameters(3)

methodstr, optional, default = None = identity transform
name of the method of DataFrame that is applied in transform
kwargsdict, optional, default = empty dict (no kwargs passed to method)
arguments passed to DataFrame.method
apply_tostr, one of “call”, “all”, “all_subset”, optional, default = “call”

“call” = method is applied to X seen in transform only “all” = method is applied to all X seen in fit, update, transform

more precisely, the application to self._X is returned

“all_subset” = method is applied to all X like for “all” value,

but before returning, result is sub-set to indices of X in transform

Examples

>>> from sktime.transformations.adapt import PandasTransformAdaptor
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = PandasTransformAdaptor ("diff")
>>> y_hat = transformer. fit_transform (y)
>>> transformer = PandasTransformAdaptor ("diff", apply_to = "all_subset")
>>> y_hat = transformer. fit (y. iloc [: 12 ])
>>> y_hat = transformer. transform (y. iloc [12:])