class_docstr: Index subsetting transformer.
  
  In transform, subsets `X` to the indices in `y.index`.
  If `y` is None, returns `X` without subsetting.
  numpy-based `X` are interpreted as having a RangeIndex starting at n,
  where n is the number of numpy rows seen so far through `fit` and `update`.
  Non-pandas types are interpreted as having index as after conversion to pandas,
  via `datatypes.convert_to`, to the `"pd.DataFrame"` sktime type.
  
  Parameters
  ----------
  index_treatment : str, optional, one of "keep" (default) or "remove"
      determines which indices are kept in `Xt = transform(X, y)`
      "keep" = all indices in y also appear in Xt. If not present in X, NA is filled.
      "remove" = only indices that appear in both X and y are present in Xt.
  
  Examples
  --------
  >>> from sktime.transformations.series.subset import IndexSubset
  >>> from sktime.datasets import load_airline
  >>> X = load_airline()[0:32]
  >>> y = load_airline()[24:42]
  >>> transformer = IndexSubset()
  >>> X_subset = transformer.fit_transform(X=X, y=y)
code: def __init__(self, index_treatment="keep"):
      self.index_treatment = index_treatment
      super().__init__()
  
file_path: sktime/sktime/transformations/series/subset.py
imports: import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 to create a new IndexSubset object from inherited base classes: BaseTransformer with example: obj = IndexSubset(index_treatment)