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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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