title_temp = "Above and Below Average Temperature for October " + str(user_year)
            title_precip = "Above and Below Average Precipitation for October " + str(user_year)
            temps_oct = get_column_values(air_temp_data, 11)
            precip_oct = get_column_values(precip_data, 11)
            mean_temp = statistics.mean(temps_oct)
            mean_precip = statistics.mean(precip_oct)
            below_mean_temp = get_located_between(air_temp_data, 11, -100, mean_temp)
            below_mean_precip = get_located_between(precip_data, 11, 0, mean_precip)
            temp_values_below = get_column_values(below_mean_temp, 11)
            precip_values_below = get_column_values(below_mean_precip, 11)
            percent_below_temp = (len(temp_values_below) / len(temps_oct))*100
            percent_above_temp = 100 - percent_below_temp
            percent_below_precip = (len(precip_values_below)/len(precip_oct))*100
            percent_above_precip = 100 - percent_below_precip
            sizes_temp = [percent_below_temp, percent_above_temp]
            sizes_precip = [percent_below_precip, percent_above_precip]


            pie_chart_infographic(title_temp, sizes_temp)
            pie_chart_infographic(title_precip, sizes_precip)

def pie_chart_infographic(title, percent_list):
    labels = ['Below the Average', 'Above the Average']
    plt.pie(percent_list, labels=labels, autopct='%1.1f%%')
    plt.title(title)
    plt.show()