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a guest Apr 24th, 2019 59 Never
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  1. title_temp = "Above and Below Average Temperature for October " + str(user_year)
  2.             title_precip = "Above and Below Average Precipitation for October " + str(user_year)
  3.             temps_oct = get_column_values(air_temp_data, 11)
  4.             precip_oct = get_column_values(precip_data, 11)
  5.             mean_temp = statistics.mean(temps_oct)
  6.             mean_precip = statistics.mean(precip_oct)
  7.             below_mean_temp = get_located_between(air_temp_data, 11, -100, mean_temp)
  8.             below_mean_precip = get_located_between(precip_data, 11, 0, mean_precip)
  9.             temp_values_below = get_column_values(below_mean_temp, 11)
  10.             precip_values_below = get_column_values(below_mean_precip, 11)
  11.             percent_below_temp = (len(temp_values_below) / len(temps_oct))*100
  12.             percent_above_temp = 100 - percent_below_temp
  13.             percent_below_precip = (len(precip_values_below)/len(precip_oct))*100
  14.             percent_above_precip = 100 - percent_below_precip
  15.             sizes_temp = [percent_below_temp, percent_above_temp]
  16.             sizes_precip = [percent_below_precip, percent_above_precip]
  17.            
  18.            
  19.             pie_chart_infographic(title_temp, sizes_temp)
  20.             pie_chart_infographic(title_precip, sizes_precip)
  21.  
  22. def pie_chart_infographic(title, percent_list):
  23.     labels = ['Below the Average', 'Above the Average']
  24.     plt.pie(percent_list, labels=labels, autopct='%1.1f%%')
  25.     plt.title(title)
  26.     plt.show()
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