class_docstr: Replaces outliers in data with NaN.
  Outliers are determined by z-score cutoff. Columns are handled independently.
  
  Parameters
  ----------
  z_cutoff : `float` or None, default None
      z-score cutoff to define outliers. If None, this transformer is a no-op.
  use_fit_baseline : `bool`, default False
      If True, the z-scores are calculated using the mean and standard
      deviation of the dataset passed to ``fit``.
  
      If False, the transformer is stateless. z-scores are calculated
      for the dataset passed to ``transform``, regardless of ``fit``.
  
  Attributes
  ----------
  mean : `pandas.Series`
      Mean of each column. NaNs are ignored.
  std : `pandas.Series`
      Standard deviation of each column. NaNs are ignored.
  _is_fitted : `bool`
      Whether the transformer is fitted.
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docstr: Computes the column mean and standard deviation,
  stored as ``mean`` and ``std`` attributes.
  
  Parameters
  ----------
  X : `pandas.DataFrame`
      Training input data. e.g. each column is a timeseries.
      Columns are expected to be numeric.
  y : None
      There is no need of a target in a transformer, yet the pipeline API
      requires this parameter.
  
  Returns
  -------
  self : object
      Returns self.
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code: def fit(self, X, y=None):
      """Computes the column mean and standard deviation,
      stored as ``mean`` and ``std`` attributes.
  
      Parameters
      ----------
      X : `pandas.DataFrame`
          Training input data. e.g. each column is a timeseries.
          Columns are expected to be numeric.
      y : None
          There is no need of a target in a transformer, yet the pipeline API
          requires this parameter.
  
      Returns
      -------
      self : object
          Returns self.
      """
      assert isinstance(X, pd.DataFrame)
      self._is_fitted = True
      if self.z_cutoff is not None and self.use_fit_baseline:
          self.mean = X.mean()
          self.std = X.std()
      return self
  
file_path: greykite/greykite/sklearn/transform/zscore_outlier_transformer.py
imports: import numpy
  import pandas
  from sklearn.base import BaseEstimator
  from sklearn.base import TransformerMixin
  from sklearn.exceptions import NotFittedError
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
label_desc: Use image node_id 2 for calling the ZscoreOutlierTransformer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self