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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code: def __init__(self, z_cutoff=None, use_fit_baseline=False):
      # sets params without modification to ensure get_params() works in grid search
      self.z_cutoff = z_cutoff
      self.use_fit_baseline = use_fit_baseline
  
      self.mean = None
      self.std = None
      self._is_fitted = None
  
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 1 to create a new ZscoreOutlierTransformer object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = ZscoreOutlierTransformer(z_cutoff, use_fit_baseline)