ata.

        Examples
        --------
        When we roll a die 6000 times, we expect to get each value around 1000
        times. But when we roll two dice and sum the result, the distribution
        is going to be quite different. A histogram illustrates those
        distributions.

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame(
            ...     np.random.randint(1, 7, 6000),
            ...     columns = ['one'])
            >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
            >>> ax = df.plot.hist(bins=12, alpha=0.5)

        A grouped histogram can be generated by providing the parameter `by` (which
        can be a column name, or a list of column names):

        .. plot::
            :context: close-figs

            >>> age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85]
            >>> df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list})
            >>> ax = df.plot.hist(column=["age"], by="gender", figsize=(10, 8))
        rI