class_docstr: Concatenates results of multiple transformer objects.
  Transformers are expected to have pd.DataFrame as input and output
  
  Modified from sklearn.pipeline.FeatureUnion
  
  This estimator applies a list of transformer objects in parallel to the
  input data, then concatenates the results. This is useful to combine
  several feature extraction mechanisms into a single transformer.
  
  Parameters of the transformers may be set using its name and the parameter
  name separated by a '__'. A transformer may be replaced entirely by
  setting the parameter with its name to another transformer,
  or removed by setting to 'drop' or ``None``.
  
  Read more in the :ref:`User Guide <feature_union>`.
  
  Parameters
  ----------
  transformer_list : list of (string, transformer) tuples
      List of transformer objects to be applied to the data. The first
      half of each tuple is the name of the transformer.
      .. versionchanged:: 0.22
         Deprecated `None` as a transformer in favor of 'drop'.
  n_jobs : int, default=None
      Number of jobs to run in parallel.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
      .. versionchanged:: v0.20
         `n_jobs` default changed from 1 to None
  transformer_weights : dict, default=None
      Multiplicative weights for features per transformer.
      Keys are transformer names, values the weights.
  verbose : bool, default=False
      If True, the time elapsed while fitting each transformer will be
      printed as it is completed.
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docstr: Fits all transformers, transforms the data and concatenates results.
  
  Modified from `sklearn.pipeline.FeatureUnion`.
  
  Parameters
  ----------
  X : `pandas.DataFrame`
      Input data to be transformed.
  
  y : array-like, shape (n_samples, ...), optional
      Targets for supervised learning.
  
  Returns
  -------
  X_t : `pandas.Dataframe`, shape (n_samples, sum_n_components)
      column-wise concatenation of results of transformers. sum_n_components is the
      sum of n_components (output dimension) over transformers.
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code: def fit_transform(self, X, y=None, **fit_params):
      """Fits all transformers, transforms the data and concatenates results.
  
      Modified from `sklearn.pipeline.FeatureUnion`.
  
      Parameters
      ----------
      X : `pandas.DataFrame`
          Input data to be transformed.
  
      y : array-like, shape (n_samples, ...), optional
          Targets for supervised learning.
  
      Returns
      -------
      X_t : `pandas.Dataframe`, shape (n_samples, sum_n_components)
          column-wise concatenation of results of transformers. sum_n_components is the
          sum of n_components (output dimension) over transformers.
      """
      results = self._parallel_func(
          X, y, fit_params, _fit_transform_one
      )
  
      if not results:
          # All transformers are None
          return pd.DataFrame(np.zeros((X.shape[0], 0)))
      Xs, transformers = zip(*results)
      self._update_transformer_list(transformers)
      Xs = pd.concat(Xs, axis=1)
      return Xs
  
file_path: greykite/greykite/sklearn/transform/pandas_feature_union.py
imports: import numpy
  import pandas
  from sklearn.pipeline import FeatureUnion
  from sklearn.pipeline import _fit_transform_one
  from sklearn.pipeline import _transform_one
  from sklearn.utils._joblib import Parallel
  from sklearn.utils._joblib import delayed
label_desc: Use image node_id 2 for calling the PandasFeatureUnion obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: Xs, pd