class_docstr: Column selection transformer.
  
  In transform, subsets `X` to `columns` provided as hyper-parameters.
  
  Sequence of columns in `Xt=transform(X)` is as in `columns` hyper-parameter.
  Caveat: this means that `transform` may change sequence of columns,
      even if no columns are removed from `X` in `transform(X)`.
  
  Parameters
  ----------
  columns : pandas compatible index or index coercible, optional, default = None
      columns to which X in transform is to be subset
  integer_treatment : str, optional, one of "col" (default) and "coerce"
      determines how integer index columns are treated
      "col" = subsets by column iloc index, even if columns is not in X.columns
      "coerce" = coerces to integer pandas.Index and attempts to subset
  index_treatment : str, optional, one of "remove" (default) or "keep"
      determines which column are kept in `Xt = transform(X, y)`
      "remove" = only indices that appear in both X and columns are present in Xt.
      "keep" = all indices in columns appear in Xt. If not present in X, NA is filled.
  
  Examples
  --------
  >>> from sktime.transformations.series.subset import ColumnSelect
  >>> from sktime.datasets import load_longley
  >>> X = load_longley()[1]
  >>> transformer =  ColumnSelect(columns=["GNPDEFL", "POP", "FOO"])
  >>> X_subset = transformer.fit_transform(X=X)
code: def __init__(
      self,
      columns=None,
      integer_treatment="col",
      index_treatment="remove",
  ):
      self.columns = columns
      self.integer_treatment = integer_treatment
      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 ColumnSelect object from inherited base classes: BaseTransformer with example: obj = ColumnSelect(columns, integer_treatment, index_treatment)