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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 bar(df, figsize=(24, 10), fontsize=16, labels=None, log=False, color='dimgray', inline=False, filter=None, n=0, p=0, sort=None):
 """ A bar chart visualization of the nullity of the given DataFrame. :param df: The input DataFrame. :param log: Whether or not to display a logorithmic plot. Defaults to False (linear). :param filter: The filter to apply to the heatmap. Should be one of "top", "bottom", or None (default). :param n: The cap on the number of columns to include in the filtered DataFrame. :param p: The cap on the percentage fill of the columns in the filtered DataFrame. :param sort: The sort to apply to the heatmap. Should be one of "ascending", "descending", or None (default). :param figsize: The size of the figure to display. :param fontsize: The figure's font size. This default to 16. :param labels: Whether or not to display the column names. Would need to be turned off on particularly large displays. Defaults to True. :param color: The color of the filled columns. Default to the RGB multiple `(0.25, 0.25, 0.25)`. :return: If `inline` is False, the underlying `matplotlib.figure` object. Else, nothing. """

    nullity_counts = len(df) - df.isnull().sum()
    df = nullity_filter(df, filter=filter, n=n, p=p)
    df = nullity_sort(df, sort=sort)

    plt.figure(figsize=figsize)
    (nullity_counts / len(df)).plot(kind='bar', figsize=figsize, fontsize=fontsize, log=log, color=color)
    ax1 = plt.gca()

    axes = [ax1]

    # Start appending elements, starting with a modified bottom x axis.
    if labels or (labels is None and len(df.columns) <= 50):
        ax1.set_xticklabels(ax1.get_xticklabels(), rotation=45, ha='right', fontsize=fontsize)

        # Create the numerical ticks.
        ax2 = ax1.twinx()
        axes.append(ax2)
        if not log:
            ax1.set_ylim([0, 1])
            ax2.set_yticks(ax1.get_yticks())
            ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
        else:
            # For some reason when a logarithmic plot is specified `ax1` always contains two more ticks than actually
            # appears in the plot. The fix is to ignore the first and last entries. Also note that when a log scale
            # is used, we have to make it match the `ax1` layout ourselves.
            ax2.set_yscale('log')
            ax2.set_ylim(ax1.get_ylim())
            ax2.set_yticklabels([int(n*len(df)) for n in ax1.get_yticks()], fontsize=fontsize)
    else:
        ax1.set_xticks([])

    # Create the third axis, which displays columnar totals above the rest of the plot.
    ax3 = ax1.twiny()
    axes.append(ax3)
    ax3.set_xticks(ax1.get_xticks())
    ax3.set_xlim(ax1.get_xlim())
    ax3.set_xticklabels(nullity_counts.values, fontsize=fontsize, rotation=45, ha='left')
    ax3.grid(False)

    for ax in axes:
        ax.spines['top'].set_visible(False)
        ax.spines['right'].set_visible(False)
        ax.spines['bottom'].set_visible(False)
        ax.spines['left'].set_visible(False)
        ax.xaxis.set_ticks_position('none')
        ax.yaxis.set_ticks_position('none')

    if inline:
        plt.show()
    else:
        return ax1