
def smushplot(data, smusher='PCA', x=1, y=2, n_components=2, marker='o',
              marker_order=None, text=False, text_order=None, linewidth=1,
              linewidth_order=None, edgecolor='k', edgecolor_order=None,
              smusher_kws=None, plot_kws=None):
    """Plot high dimensional data in 2d space

    Parameters
    ----------
    data : pandas.DataFrame or numpy.array
        A (n_samples, m_features) wide matrix of observations. The samples
        (rows) will be plotted relative to the reduced representation of the
        features (columns)
    smusher : str or object
        Either a string specifying a valid dimensionality reduction algorithm
        in ``sklearn.decomposition`` or ``sklearn.manifold``, or any object
        with ``fit_transform()`` methods.


    Notes
    -----


    """
    if isinstance(smusher, str):
        # Need to get appropriate smusher from sklearn given the string
        pass
    else:
        # Assume this is already an initialized sklearn object with the
        # ``fit_transform()`` method
        pass


def smushplot(data, smusher='PCA', x=1, y=2, n_components=2, marker='o',
              marker_order=None, text=False, text_order=None, linewidth=1,
              linewidth_order=None, edgecolor='k', edgecolor_order=None,
              smusher_kws=None, plot_kws=None):
    """Plot high dimensional data in 2d space

    Parameters
    ----------
    data : pandas.DataFrame or numpy.array
        A (n_samples, m_features) wide matrix of observations. The samples
        (rows) will be plotted relative to the reduced representation of the
        features (columns)
    smusher : str or object
        Either a string specifying a valid dimensionality reduction algorithm
        in ``sklearn.decomposition`` or ``sklearn.manifold``, or any object
        with ``fit_transform()`` methods.
    x, y : int
        1-based counting of which components to plot as the x- and y-axes. For
        example, to plot component 4 on the x-axis and component 10 on the y,
        do ``x=4, y=10``.
    n_components : int
        Number of components to use when reducing dimensionality

    Notes
    -----


    """
    if isinstance(smusher, str):
        # Need to get appropriate smusher from sklearn given the string
        pass
    else:
        # Assume this is already an initialized sklearn object with the
        # ``fit_transform()`` method
        pass

Add x, y and n_components to docstring
Add x, y and n_components to docstring

  
  def smushplot(data, smusher='PCA', x=1, y=2, n_components=2, marker='o',
                marker_order=None, text=False, text_order=None, linewidth=1,
                linewidth_order=None, edgecolor='k', edgecolor_order=None,
                smusher_kws=None, plot_kws=None):
      """Plot high dimensional data in 2d space
  
      Parameters
      ----------
      data : pandas.DataFrame or numpy.array
          A (n_samples, m_features) wide matrix of observations. The samples
          (rows) will be plotted relative to the reduced representation of the
          features (columns)
      smusher : str or object
          Either a string specifying a valid dimensionality reduction algorithm
          in ``sklearn.decomposition`` or ``sklearn.manifold``, or any object
          with ``fit_transform()`` methods.
- 
+     x, y : int
+         1-based counting of which components to plot as the x- and y-axes. For
+         example, to plot component 4 on the x-axis and component 10 on the y,
+         do ``x=4, y=10``.
+     n_components : int
+         Number of components to use when reducing dimensionality
  
      Notes
      -----
  
  
      """
      if isinstance(smusher, str):
          # Need to get appropriate smusher from sklearn given the string
          pass
      else:
          # Assume this is already an initialized sklearn object with the
          # ``fit_transform()`` method
          pass
  
Add x, y and n_components to docstring
## Code Before:

def smushplot(data, smusher='PCA', x=1, y=2, n_components=2, marker='o',
              marker_order=None, text=False, text_order=None, linewidth=1,
              linewidth_order=None, edgecolor='k', edgecolor_order=None,
              smusher_kws=None, plot_kws=None):
    """Plot high dimensional data in 2d space

    Parameters
    ----------
    data : pandas.DataFrame or numpy.array
        A (n_samples, m_features) wide matrix of observations. The samples
        (rows) will be plotted relative to the reduced representation of the
        features (columns)
    smusher : str or object
        Either a string specifying a valid dimensionality reduction algorithm
        in ``sklearn.decomposition`` or ``sklearn.manifold``, or any object
        with ``fit_transform()`` methods.


    Notes
    -----


    """
    if isinstance(smusher, str):
        # Need to get appropriate smusher from sklearn given the string
        pass
    else:
        # Assume this is already an initialized sklearn object with the
        # ``fit_transform()`` method
        pass

## Instruction:
Add x, y and n_components to docstring
## Code After:

def smushplot(data, smusher='PCA', x=1, y=2, n_components=2, marker='o',
              marker_order=None, text=False, text_order=None, linewidth=1,
              linewidth_order=None, edgecolor='k', edgecolor_order=None,
              smusher_kws=None, plot_kws=None):
    """Plot high dimensional data in 2d space

    Parameters
    ----------
    data : pandas.DataFrame or numpy.array
        A (n_samples, m_features) wide matrix of observations. The samples
        (rows) will be plotted relative to the reduced representation of the
        features (columns)
    smusher : str or object
        Either a string specifying a valid dimensionality reduction algorithm
        in ``sklearn.decomposition`` or ``sklearn.manifold``, or any object
        with ``fit_transform()`` methods.
    x, y : int
        1-based counting of which components to plot as the x- and y-axes. For
        example, to plot component 4 on the x-axis and component 10 on the y,
        do ``x=4, y=10``.
    n_components : int
        Number of components to use when reducing dimensionality

    Notes
    -----


    """
    if isinstance(smusher, str):
        # Need to get appropriate smusher from sklearn given the string
        pass
    else:
        # Assume this is already an initialized sklearn object with the
        # ``fit_transform()`` method
        pass
