def rank1d(X, y=None, ax=None, algorithm='shapiro', features=None,
           orient='h', show_feature_names=True, **kwargs):
    """Scores each feature with the algorithm and ranks them in a bar plot.

    This helper function is a quick wrapper to utilize the Rank1D Visualizer
    (Transformer) for one-off analysis.

    Parameters
    ----------
    X : ndarray or DataFrame of shape n x m
        A matrix of n instances with m features

    y : ndarray or Series of length n
        An array or series of target or class values

    ax : matplotlib axes
        the axis to plot the figure on.

    algorithm : one of {'shapiro', }, default: 'shapiro'
        The ranking algorithm to use, default is 'Shapiro-Wilk.

    features : list
        A list of feature names to use.
        If a DataFrame is passed to fit and features is None, feature
        names are selected as the columns of the DataFrame.

    orient : 'h' or 'v'
        Specifies a horizontal or vertical bar chart.

    show_feature_names : boolean, default: True
        If True, the feature names are used to label the axis ticks in the
        plot.

    Returns
    -------
    ax : matplotlib axes
        Returns the axes that the parallel coordinates were drawn on.

    """
    # Instantiate the visualizer
    visualizer = Rank1D(ax, algorithm, features, orient, show_feature_names,
                        **kwargs)

    # Fit and transform the visualizer (calls draw)
    visualizer.fit(X, y, **kwargs)
    visualizer.transform(X)

    # Return the axes object on the visualizer
    return visualizer.ax
def rank1d ( X , y = None , ax = None , algorithm = 'shapiro' , features = None , orient = 'h' , show_feature_names = True , ** kwargs ) : visualizer = Rank1D ( ax , algorithm , features , orient , show_feature_names , ** kwargs ) visualizer . fit ( X , y , ** kwargs ) visualizer . transform ( X ) return visualizer . ax
Scores each feature with the algorithm and ranks them in a bar plot.

    This helper function is a quick wrapper to utilize the Rank1D Visualizer
    (Transformer) for one-off analysis.

    Parameters
    ----------
    X : ndarray or DataFrame of shape n x m
        A matrix of n instances with m features

    y : ndarray or Series of length n
        An array or series of target or class values

    ax : matplotlib axes
        the axis to plot the figure on.

    algorithm : one of {'shapiro', }, default: 'shapiro'
        The ranking algorithm to use, default is 'Shapiro-Wilk.

    features : list
        A list of feature names to use.
        If a DataFrame is passed to fit and features is None, feature
        names are selected as the columns of the DataFrame.

    orient : 'h' or 'v'
        Specifies a horizontal or vertical bar chart.

    show_feature_names : boolean, default: True
        If True, the feature names are used to label the axis ticks in the
        plot.

    Returns
    -------
    ax : matplotlib axes
        Returns the axes that the parallel coordinates were drawn on.