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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 plot_monthly_ic_heatmap(mean_monthly_ic, ax=None):
 """ Plots a heatmap of the information coefficient or returns by month. Parameters mean_monthly_ic : pd.DataFrame The mean monthly IC for N periods forward. Returns ------- ax : matplotlib.Axes The axes that were plotted on. """


    mean_monthly_ic = mean_monthly_ic.copy()

    num_plots = len(mean_monthly_ic.columns)

    v_spaces = ((num_plots - 1) // 3) + 1

    if ax is None:
        f, ax = plt.subplots(v_spaces, 3, figsize=(18, v_spaces * 6))
        ax = ax.flatten()

    new_index_year = []
    new_index_month = []
    for date in mean_monthly_ic.index:
        new_index_year.append(date.year)
        new_index_month.append(date.month)

    mean_monthly_ic.index = pd.MultiIndex.from_arrays(
        [new_index_year, new_index_month],
        names=["year", "month"])

    for a, (periods_num, ic) in zip(ax, mean_monthly_ic.iteritems()):

        sns.heatmap(
            ic.unstack(),
            annot=True,
            alpha=1.0,
            center=0.0,
            annot_kws={"size": 7},
            linewidths=0.01,
            linecolor='white',
            cmap=cm.coolwarm_r,
            cbar=False,
            ax=a)
        a.set(ylabel='', xlabel='')

        a.set_title("Monthly Mean {} Period IC".format(periods_num))

    if num_plots < len(ax):
        ax[-1].set_visible(False)

    return ax