class_docstr: Drop missing values transformation.
  
  Drops rows or columns with missing values from X. Mostly wraps
  pandas.DataFrame.dropna, but allows specifying thresh as a fraction of
  non-missing observations.
  
  Parameters
  ----------
  axis : {0 or 'index', 1 or 'columns'}, default 0
      Determine if rows or columns which contain missing values are removed.
      Must be 0 or 'index' for univariate input.
  
      * 0, or 'index' : Drop rows which contain missing values.
      * 1, or 'columns' : Drop columns which contain missing value.
  
  how : {'any', 'all'}, default 'any'
      Determine if row or column is removed from DataFrame, when we have
      at least one NA or all NA.
  
      * 'any' : If any NA values are present, drop that row or column.
      * 'all' : If all values are NA, drop that row or column.
  
  thresh : int or float, optional
       If int, require at least that many non-NA values (as in pandas.dropna).
       If float, minimum share of non-NA values for rows/columns to be
       retained. Fraction must be contained within (0,1]. Setting fraction
       to 1.0 is equivalent to setting how='any'. thresh cannot be combined
       with how.
  
  remember : bool, default False if axis==0, True if axis==1
      If True, drops the same rows/columns in transform as in fit. If false,
      drops rows/columns according to the NAs seen in transform (equivalent
      to PandasTransformAdaptor(method="dropna")).
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docstr: Transform X and return a transformed version.
  
  private _transform containing core logic, called from transform
  
  Parameters
  ----------
  X : pd.DataFrame
      if self.get_tag("univariate-only")==True:
          guaranteed to have a single column
      if self.get_tag("univariate-only")==False: no restrictions apply
  y : None, present only for interface compatibility
  
  Returns
  -------
  transformed version of X
docstr_tok: ["Transform","X","and","return","a","transformed","version",".","private","_transform","containing","core","logic",",","called","from","transform","Parameters","--","--","--","--","--","X",":","pd.DataFrame","if","self.get_tag","(","``","univariate-only","''",")","==True",":","guaranteed","to","have","a","single","column","if","self.get_tag","(","``","univariate-only","''",")","==False",":","no","restrictions","apply","y",":","None",",","present","only","for","interface","compatibility","Returns","--","--","--","-","transformed","version","of","X"]
code: def _transform(self, X, y=None):
      """Transform X and return a transformed version.
  
      private _transform containing core logic, called from transform
  
      Parameters
      ----------
      X : pd.DataFrame
          if self.get_tag("univariate-only")==True:
              guaranteed to have a single column
          if self.get_tag("univariate-only")==False: no restrictions apply
      y : None, present only for interface compatibility
  
      Returns
      -------
      transformed version of X
      """
      dropped_index_values = self.dropped_index_values_
      agg_axis = self._agg_axis
      axis = self._axis
      how = self._how
      thresh = self._thresh
      remember = self._remember
  
      if remember:
          if dropped_index_values is not None:
              return X.drop(labels=dropped_index_values, axis=axis)
          else:
              return X
      else:
          if isinstance(thresh, float):
              mask = X.notna().mean(axis=agg_axis) < thresh
              index_to_drop = mask.index[mask]
              return X.drop(labels=index_to_drop, axis=axis)
          elif isinstance(thresh, int):
              return X.dropna(axis=axis, thresh=thresh)
          else:
              return X.dropna(axis=axis, how=how)
  
file_path: sktime/sktime/transformations/series/dropna.py
imports: from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 7 for calling the DropNA obj's underlying member method code with example usage: obj._transform(X, y) and returns: X, X, X, X, X