code: import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  #from pylab import plot, show, xlim,figure,hold, ylim,legend, boxplot, setup, axes
  
  import seaborn as sns
  
  # Is this a personal or work computer
  # Are you graphing for hood or no hood
  
  Computer = 'personal' #or 'personal' or 'work'
  Hood_or_no = 'no_hood'   # 'no_hood' or 'hood'
  #what household do you want to remove make sure it is in ascending order
  # if there is nothing, then put a placeholder of 1045 or higher
  Household_removal = [1045]
  #Household_removal = Household_removal.sort(reverse=False)
  Household_removal_NO_Hood_fuel_day_adult = [1045]
  Household_removal_Hood_fuel_day_adult = [2020]
  
  Household_removal_NO_Hood_PM = [1045]
  Household_removal_Hood_PM = [2020]
  
  
  pd.set_option('display.max_rows', 500)
  pd.set_option('display.max_columns', 500)
  pd.set_option('display.width', 1000)
  
  if Hood_or_no == 'hood':
      C_Place_holder = 2001
  else:
      C_Place_holder = 1001
      
  if Computer == 'personal' and Hood_or_no == 'no_hood':
      # 1N
      datafile_path_day_1N ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_Summary_Day_1_exact.csv"
      Day_1N = pd.read_csv(datafile_path_day_1N, skiprows=2)
      datafile_path_event_1N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_Summary_Event_1_exact.csv"
      Event_1N = pd.read_csv(datafile_path_event_1N, skiprows=2)
      # there is no second exact in phase 1N
      #1N Survey
      datafile_path_survey_1N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_1H_Survey_summary_.csv"
      Filter_1n_survey = pd.read_csv(datafile_path_survey_1N, skiprows=0)
      #print(Filter_1n_survey.iloc[0:40, :])
      Survey_1N = Filter_1n_survey.iloc[0:40,:]
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_1N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_24_hour_Kitchen_PM.csv"
      Kit_PM_1N_24hr = pd.read_csv(data_file_path_24_PM_1N, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_Fuel_1N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_24_hour_Fuel_removal.csv"
      Fuel_remove_1N_24hr = pd.read_csv(data_file_path_24_Fuel_1N, skiprows=0)
      
      #2N
      datafile_path_day_2N ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_Summary_Day_1_exact.csv"
      Day_2N = pd.read_csv(datafile_path_day_2N, skiprows=2)
      datafile_path_event_2N_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_Summary_Event_1_exact.csv"
      Event_2N_1 = pd.read_csv(datafile_path_event_2N_1, skiprows=2)
      #2N second Exact
      datafile_path_event_2N_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_Summary_Event_2_exact.csv"
      Event_2N_2 = pd.read_csv(datafile_path_event_2N_2, skiprows=2)
      #2N Survey
      datafile_path_survey_2N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_Survey_summary_.csv"
      Survey_2N = pd.read_csv(datafile_path_survey_2N, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_2N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_24_hour_Kitchen_PM.csv"
      Kit_PM_2N_24hr = pd.read_csv(data_file_path_24_PM_2N, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_Fuel_2N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2N/2N_24_hour_Fuel_removal.csv"
      Fuel_remove_2N_24hr = pd.read_csv(data_file_path_24_Fuel_2N, skiprows=0)
      
      #3N
      datafile_path_day_3N ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_Summary_Day_1_exact.csv"
      Day_3N = pd.read_csv(datafile_path_day_3N, skiprows=2)
      datafile_path_event_3N_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_Summary_Event_1_exact.csv"
      Event_3N_1 = pd.read_csv(datafile_path_event_3N_1, skiprows=2)
      #3N second Exact
      datafile_path_event_3N_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_Summary_Event_2_exact.csv"
      Event_3N_2 = pd.read_csv(datafile_path_event_3N_2, skiprows=2)
      #3N Survey 
      datafile_path_survey_3N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_Survey_summary_.csv"
      Survey_3N = pd.read_csv(datafile_path_survey_3N, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_3N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_24_hour_Kitchen_PM.csv"
      Kit_PM_3N_24hr = pd.read_csv(data_file_path_24_PM_3N, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_Fuel_3N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3N/3N_24_hour_Fuel_removal.csv"
      Fuel_remove_3N_24hr = pd.read_csv(data_file_path_24_Fuel_3N, skiprows=0)
      
      #4N
      datafile_path_day_4N ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_Summary_Day_1_exact.csv"
      Day_4N = pd.read_csv(datafile_path_day_4N, skiprows=2)
      datafile_path_event_4N_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_Summary_Event_1_exact.csv"
      Event_4N_1 = pd.read_csv(datafile_path_event_4N_1, skiprows=2)
      #4N second Exact
      datafile_path_event_4N_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_Summary_Event_2_exact.csv"
      Event_4N_2 = pd.read_csv(datafile_path_event_4N_2, skiprows=2)
      #4N Survey 
      datafile_path_survey_4N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_Survey_summary_.csv"
      Survey_4N = pd.read_csv(datafile_path_survey_4N, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_4N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_24_hour_Kitchen_PM.csv"
      Kit_PM_4N_24hr = pd.read_csv(data_file_path_24_PM_4N, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_Fuel_4N = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/4N/4N_24_hour_Fuel_removal.csv"
      Fuel_remove_4N_24hr = pd.read_csv(data_file_path_24_Fuel_4N, skiprows=0)
      
  elif Computer == 'personal' and Hood_or_no == 'hood':
      #1H
      datafile_path_day_1H ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1H/1H_Summary_Day_1_exact.csv"
      Day_1H = pd.read_csv(datafile_path_day_1H, skiprows=2)
      datafile_path_event_1H ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1H/1H_Summary_Event_1_exact.csv"
      Event_1H = pd.read_csv(datafile_path_event_1H, skiprows=2)
      #there is no second exact in phase 1H
      #1H Survey (row 40 or so afterward is Hood portion column 1 is houshold number)
      datafile_path_survey_1H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1N/1N_1H_Survey_summary_.csv"
      Survey_1H = pd.read_csv(datafile_path_survey_1H, skiprows=40)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_1H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1H/1H_24_hour_Kitchen_PM.csv"
      Kit_PM_1H_24hr = pd.read_csv(data_file_path_24_PM_1H, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_fuel_1H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/1H/1H_24_hour_Fuel_removal.csv"
      Fuel_remove_1H_24hr = pd.read_csv(data_file_path_24_fuel_1H, skiprows=0)
      
      
      #2H
      datafile_path_day_2H ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_Summary_Day_1_exact.csv"
      Day_2H = pd.read_csv(datafile_path_day_2H, skiprows=2)
      datafile_path_event_2H_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_Summary_Event_1_exact.csv"
      Event_2H_1 = pd.read_csv(datafile_path_event_2H_1, skiprows=2)
      #2H second Exact
      datafile_path_event_2H_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_Summary_Event_2_exact.csv"
      Event_2H_2 = pd.read_csv(datafile_path_event_2H_2, skiprows=2)
      #2H survey 
      datafile_path_survey_2H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_Survey_summary_.csv"
      Survey_2H = pd.read_csv(datafile_path_survey_2H, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_2H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_24_hour_Kitchen_PM.csv"
      Kit_PM_2H_24hr = pd.read_csv(data_file_path_24_PM_2H, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_fuel_2H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/2H/2H_24_hour_Fuel_removal.csv"
      Fuel_remove_2H_24hr = pd.read_csv(data_file_path_24_fuel_2H, skiprows=0)
      
      #3H
      datafile_path_day_3H ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_Summary_Day_1_exact.csv"
      Day_3H = pd.read_csv(datafile_path_day_3H, skiprows=2)
      datafile_path_event_3N_1 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_Summary_Event_1_exact.csv"
      Event_3H_1 = pd.read_csv(datafile_path_event_3N_1, skiprows=2)
      #3H second Exact
      datafile_path_event_3H_2 ="C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_Summary_Event_2_exact.csv"
      Event_3H_2 = pd.read_csv(datafile_path_event_3H_2, skiprows=2)
      #3H survey 
      datafile_path_survey_3H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_Survey_summary_.csv"
      Survey_3H = pd.read_csv(datafile_path_survey_3H, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_3H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_24_hour_Kitchen_PM.csv"
      Kit_PM_3H_24hr = pd.read_csv(data_file_path_24_PM_3H, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_fuel_3H = "C:/Users/gvros/Desktop/Oregon State Masters/Work/OSU, CSC, CQC Project files/3H/3H_24_hour_Fuel_removal.csv"
      Fuel_remove_3H_24hr = pd.read_csv(data_file_path_24_fuel_3H, skiprows=0)
      
      #work uses box information and not local data
  elif Computer == 'work' and Hood_or_no == 'no_hood':
      # 1N for box file system
      datafile_path_day_1N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_Summary_Day_1_exact.csv"
      Day_1N = pd.read_csv(datafile_path_day_1N, skiprows=2)
      datafile_path_event_1N ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_Summary_Event_1_exact.csv"
      Event_1N = pd.read_csv(datafile_path_event_1N, skiprows=2)
      # there is no second exact in phase 1N
      #1N Survey 
      datafile_path_survey_1N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_1H_Survey_summary_.csv"
      Filter_1n_survey = pd.read_csv(datafile_path_survey_1N, skiprows=0)
      #print(Filter_1n_survey.iloc[0:40, :])
      Survey_1N = Filter_1n_survey.iloc[0:40,:]
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_1N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_24_hour_Kitchen_PM.csv"
      Kit_PM_1N_24hr = pd.read_csv(data_file_path_24_PM_1N, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_Fuel_1N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_24_hour_Fuel_removal.csv"
      Fuel_remove_1N_24hr = pd.read_csv(data_file_path_24_Fuel_1N, skiprows=0)
      
      #2N
      datafile_path_day_2N ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_Summary_Day_1_exact.csv"
      Day_2N = pd.read_csv(datafile_path_day_2N, skiprows=2)
      datafile_path_event_2N_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_Summary_Event_1_exact.csv"
      Event_2N_1 = pd.read_csv(datafile_path_event_2N_1, skiprows=2)
      #2N second Exact
      datafile_path_event_2N_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_Summary_Event_2_exact.csv"
      Event_2N_2 = pd.read_csv(datafile_path_event_2N_2, skiprows=2)
      #2N Survey
      datafile_path_survey_2N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_Survey_summary_.csv"
      Survey_2N = pd.read_csv(datafile_path_survey_2N, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_2N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_24_hour_Kitchen_PM.csv"
      Kit_PM_2N_24hr = pd.read_csv(data_file_path_24_PM_2N, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_Fuel_2N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2N/2N_24_hour_Fuel_removal.csv"
      Fuel_remove_2N_24hr = pd.read_csv(data_file_path_24_Fuel_2N, skiprows=0)
      
      #3N
      datafile_path_day_3N ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_Summary_Day_1_exact.csv"
      Day_3N = pd.read_csv(datafile_path_day_3N, skiprows=2)
      datafile_path_event_3N_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_Summary_Event_1_exact.csv"
      Event_3N_1 = pd.read_csv(datafile_path_event_3N_1, skiprows=2)
      #3N second Exact
      datafile_path_event_3N_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_Summary_Event_2_exact.csv"
      Event_3N_2 = pd.read_csv(datafile_path_event_3N_2, skiprows=2)
      #3N survey
      datafile_path_survey_3N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_Survey_summary_.csv"
      Survey_3N = pd.read_csv(datafile_path_survey_3N, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_3N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_24_hour_Kitchen_PM.csv"
      Kit_PM_3N_24hr = pd.read_csv(data_file_path_24_PM_3N, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_Fuel_3N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3N/3N_24_hour_Fuel_removal.csv"
      Fuel_remove_3N_24hr = pd.read_csv(data_file_path_24_Fuel_3N, skiprows=0)
      
      #4N
      datafile_path_day_4N ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_Summary_Day_1_exact.csv"
      Day_4N = pd.read_csv(datafile_path_day_4N, skiprows=2)
      datafile_path_event_4N_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_Summary_Event_1_exact.csv"
      Event_4N_1 = pd.read_csv(datafile_path_event_4N_1, skiprows=2)
      #4N second Exact
      datafile_path_event_4N_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_Summary_Event_2_exact.csv"
      Event_4N_2 = pd.read_csv(datafile_path_event_4N_2, skiprows=2)
      #4N Survey 
      datafile_path_survey_4N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_Survey_summary_.csv"
      Survey_4N = pd.read_csv(datafile_path_survey_4N, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_4N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_24_hour_Kitchen_PM.csv"
      Kit_PM_4N_24hr = pd.read_csv(data_file_path_24_PM_4N, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_Fuel_4N = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/4N/4N_24_hour_Fuel_removal.csv"
      Fuel_remove_4N_24hr = pd.read_csv(data_file_path_24_Fuel_4N, skiprows=0)
      
  else:
      #1H
      datafile_path_day_1H ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1H/1H_Summary_Day_1_exact.csv"
      Day_1H = pd.read_csv(datafile_path_day_1H, skiprows=2)
      datafile_path_event_1H ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1H/1H_Summary_Event_1_exact.csv"
      Event_1H = pd.read_csv(datafile_path_event_1H, skiprows=2)
      #there is no second exact in phase 1H
      #1H Survey (row 40 or so afterward is Hood portion column 1 is houshold number)
      datafile_path_survey_1H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1N/1N_1H_Survey_summary_.csv"
      Survey_1H = pd.read_csv(datafile_path_survey_1H, skiprows=40)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_1H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1H/1H_24_hour_Kitchen_PM.csv"
      Kit_PM_1H_24hr = pd.read_csv(data_file_path_24_PM_1H, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_fuel_1H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/1H/1H_24_hour_Fuel_removal.csv"
      Fuel_remove_1H_24hr = pd.read_csv(data_file_path_24_fuel_1H, skiprows=0)
      
      #2H
      datafile_path_day_2H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_Summary_Day_1_exact.csv"
      Day_2H = pd.read_csv(datafile_path_day_2H, skiprows=2)
      datafile_path_event_2H_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_Summary_Event_1_exact.csv"
      Event_2H_1 = pd.read_csv(datafile_path_event_2H_1, skiprows=2)
      #2H second Exact
      datafile_path_event_2H_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_Summary_Event_2_exact.csv"
      Event_2H_2 = pd.read_csv(datafile_path_event_2H_2, skiprows=2)
      #2H survey 
      datafile_path_survey_2H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_Survey_summary_.csv"
      Survey_2H = pd.read_csv(datafile_path_survey_2H, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_2H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_24_hour_Kitchen_PM.csv"
      Kit_PM_2H_24hr = pd.read_csv(data_file_path_24_PM_2H, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_fuel_2H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/2H/2H_24_hour_Fuel_removal.csv"
      Fuel_remove_2H_24hr = pd.read_csv(data_file_path_24_fuel_2H, skiprows=0)
      
      #3H
      datafile_path_day_3H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_Summary_Day_1_exact.csv"
      Day_3H = pd.read_csv(datafile_path_day_3H, skiprows=2)
      datafile_path_event_3N_1 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_Summary_Event_1_exact.csv"
      Event_3H_1 = pd.read_csv(datafile_path_event_3N_1, skiprows=2)
      #3H second Exact
      datafile_path_event_3H_2 ="C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_Summary_Event_2_exact.csv"
      Event_3H_2 = pd.read_csv(datafile_path_event_3H_2, skiprows=2)
      #3H survey 
      datafile_path_survey_3H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_Survey_summary_.csv"
      Survey_3H = pd.read_csv(datafile_path_survey_3H, skiprows=0)
      #24 hour Kitchen pm breakdown
      data_file_path_24_PM_3H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_24_hour_Kitchen_PM.csv"
      Kit_PM_3H_24hr = pd.read_csv(data_file_path_24_PM_3H, skiprows=0)
      #24 hour Fuel Removal breakdown
      data_file_path_24_fuel_3H = "C:/Users/rossgra/Box/OSU, CSC, CQC Project files/3H/3H_24_hour_Fuel_removal.csv"
      Fuel_remove_3H_24hr = pd.read_csv(data_file_path_24_fuel_3H, skiprows=0)
      
  #time to start ploting fun things 
  #1st starting with the fuel per day per adult histogram and box plot
  NO_hood_counter = np.arange(0,39)
  hood_counter = np.arange(0,14)
  #what household do you want to remove from the graphs (1046 is a dummy spacer)
  
  
  print('---------------Fuel per Day per Adult No-Hood Phase---------------------')
  if Hood_or_no == 'no_hood':
      Fuel_per_day_per_adult_1N = []
      f_d_a_1N = []
      Fuel_per_day_per_adult_2N = []
      f_d_a_2N = []
      Fuel_per_day_per_adult_3N = []
      f_d_a_3N = []
      Fuel_per_day_per_adult_4N = []
      f_d_a_4N =[]
      count_t = 0
      count_f = 0
      for c in NO_hood_counter:
          if c == (Household_removal[count_t] - C_Place_holder):
              count_t = count_t + 1
              if count_t == len(Household_removal):
                  count_t = 0
              continue
          if c == (Household_removal_NO_Hood_fuel_day_adult[count_f] - C_Place_holder):
              count_f = count_f + 1
              if count_f == len(Household_removal_NO_Hood_fuel_day_adult):
                  count_f = 0
              continue
          if Fuel_remove_1N_24hr.iloc[c,6]!= -1.00:
              Fuel_per_day_per_adult_1N.append(Fuel_remove_1N_24hr.iloc[c,6]/Survey_1N.iloc[c,7])
              f_d_a_1N.append(Day_1N.iloc[c,0])
          if Fuel_remove_2N_24hr.iloc[c,6] != -1.00:
              Fuel_per_day_per_adult_2N.append(Fuel_remove_2N_24hr.iloc[c,6] / Survey_2N.iloc[c, 7])
              f_d_a_2N.append(Day_2N.iloc[c,0])
          if Fuel_remove_3N_24hr.iloc[c,6] != -1.00:
              Fuel_per_day_per_adult_3N.append(Fuel_remove_3N_24hr.iloc[c,6]/ Survey_3N.iloc[c, 7])
              f_d_a_3N.append(Day_3N.iloc[c, 0])
          if Fuel_remove_4N_24hr.iloc[c,6]  != -1.00:
              Fuel_per_day_per_adult_4N.append(Fuel_remove_4N_24hr.iloc[c,6] / Survey_4N.iloc[c, 7])
              f_d_a_4N.append(Day_3N.iloc[c, 0])
      # percentage Change of Fuel per day between the phases
      Fuel_per_day_per_adult_2N_1N = []
      f_d_a_2N_1N = []
      Fuel_per_day_per_adult_3N_1N = []
      f_d_a_3N_1N = []
      Fuel_per_day_per_adult_4N_1N = []
      f_d_a_4N_1N = []
      
      Fuel_per_day_per_adult_3N_2N = []
      f_d_a_3N_2N = []
      Fuel_per_day_per_adult_4N_3N = []
      f_d_a_4N_3N = []
      Fuel_per_day_per_adult_4N_2N = []
      f_d_a_4N_2N = []
  
      count_t = 0
      count_f = 0
      for c in NO_hood_counter:
          if c == (Household_removal[count_t] - C_Place_holder):
              count_t = count_t + 1
              if count_t == len(Household_removal):
                  count_t = 0
              continue
          if c == (Household_removal_NO_Hood_fuel_day_adult[count_f] - C_Place_holder):
              count_f = count_f + 1
              if count_f == len(Household_removal_NO_Hood_fuel_day_adult):
                  count_f = 0
              continue
          if (len(Fuel_per_day_per_adult_2N)-1) >= c and (len(Fuel_per_day_per_adult_1N)-1) >= c:
              if Day_1N.iloc[c,13] > 0 and Day_2N.iloc[c,13]  > 0 and Day_1N.iloc[c,0] == Day_2N.iloc[c,0]:
                  Fuel_per_day_per_adult_2N_1N.append(Fuel_per_day_per_adult_2N[c]/Fuel_per_day_per_adult_1N[c])
                  f_d_a_2N_1N.append(Day_1N.iloc[c,0])
          if (len(Fuel_per_day_per_adult_3N)-1) >= c and (len(Fuel_per_day_per_adult_1N)-1) >= c:
              if Day_3N.iloc[c,13] > 0 and Day_1N.iloc[c,13]  > 0 and Day_3N.iloc[c,0] == Day_1N.iloc[c,0]:
                  Fuel_per_day_per_adult_3N_1N.append(Fuel_per_day_per_adult_3N[c]/Fuel_per_day_per_adult_1N[c])
                  f_d_a_3N_1N.append(Day_1N.iloc[c,0])
          if (len(Fuel_per_day_per_adult_4N)-1) >= c and (len(Fuel_per_day_per_adult_1N)-1) >= c:
              if Day_4N.iloc[c,13] > 0 and Day_1N.iloc[c,13]  > 0 and Day_4N.iloc[c,0] == Day_1N.iloc[c,0]:
                  Fuel_per_day_per_adult_4N_1N.append(Fuel_per_day_per_adult_4N[c]/Fuel_per_day_per_adult_1N[c])
                  f_d_a_4N_1N.append(Day_1N.iloc[c,0])
          if (len(Fuel_per_day_per_adult_3N)-1) >= c and (len(Fuel_per_day_per_adult_2N)-1) >= c:
              if Day_3N.iloc[c,13] > 0 and Day_2N.iloc[c,13]  > 0 and Day_3N.iloc[c,0] == Day_2N.iloc[c,0]:
                  Fuel_per_day_per_adult_3N_2N.append(Fuel_per_day_per_adult_3N[c]/Fuel_per_day_per_adult_2N[c])
                  f_d_a_3N_2N.append(Day_2N.iloc[c,0])
          if (len(Fuel_per_day_per_adult_4N)-1) >= c and (len(Fuel_per_day_per_adult_3N)-1) >= c:
              if Day_4N.iloc[c,13] > 0 and Day_3N.iloc[c,13]  > 0 and Day_4N.iloc[c,0] == Day_3N.iloc[c,0]:
                  Fuel_per_day_per_adult_4N_3N.append(Fuel_per_day_per_adult_4N[c]/Fuel_per_day_per_adult_3N[c])
                  f_d_a_4N_3N.append(Day_3N.iloc[c,0])
          if (len(Fuel_per_day_per_adult_4N)-1) >= c and (len(Fuel_per_day_per_adult_2N)-1) >= c:
              if Day_4N.iloc[c,13] > 0 and Day_2N.iloc[c,13]  > 0 and Day_4N.iloc[c,0] == Day_2N.iloc[c,0]:
                  Fuel_per_day_per_adult_4N_2N.append(Fuel_per_day_per_adult_4N[c]/Fuel_per_day_per_adult_2N[c])
                  f_d_a_4N_2N.append(Day_4N.iloc[c,0])
      
      
      
      # now for box plotting for Fuel per day beteen Phases
      #1N
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Fuel_per_day_per_adult_1N, ax=ax_box, color='b')
      sns.distplot(Fuel_per_day_per_adult_1N, ax=ax_hist, color='b')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('1N Fuel per Day per Adult')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      #2N
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Fuel_per_day_per_adult_2N, ax=ax_box, color='g')
      sns.distplot(Fuel_per_day_per_adult_2N, ax=ax_hist, color='g')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('2N Fuel per Day per Adult')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      #3N
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Fuel_per_day_per_adult_3N, ax=ax_box, color='r')
      sns.distplot(Fuel_per_day_per_adult_3N, ax=ax_hist, color='r')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('3N Fuel per Day per Adult')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      #4N
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Fuel_per_day_per_adult_4N, ax=ax_box, color='y')
      sns.distplot(Fuel_per_day_per_adult_4N, ax=ax_hist, color='y')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('4N Fuel per Day per Adult')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
     
      #Plotting on the same graph
      fig, ax = plt.subplots()
      plt.title('No-Hood Fuel per Day per Adult')
      #plt.hold(True)
      #1N
      quant_1_1N = np.percentile(Fuel_per_day_per_adult_1N, [25,50,75])
      Top_lim_1_1N = quant_1_1N[2] + 1.5*(quant_1_1N[2] - quant_1_1N[0])
      Low_lim_1_1N = quant_1_1N[0] - 1.5*(quant_1_1N[2] - quant_1_1N[0])
      
      bp_1 = plt.boxplot(Fuel_per_day_per_adult_1N, positions = [1], widths = 0.6)
      Fuel_D_A_1N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_1N):
          if a > Top_lim_1_1N or a < Low_lim_1_1N:
              Fuel_D_A_1N_outlier.append(f_d_a_1N[v])
              plt.text(1,a,f_d_a_1N[v])
      plt.text(1,0.1,'1N',color='b')
  
      #2N    
      quant_1_2N = np.percentile(Fuel_per_day_per_adult_2N, [25,50,75])
      Top_lim_1_2N = quant_1_2N[2] + 1.5*(quant_1_2N[2] - quant_1_2N[0])
      Low_lim_1_2N = quant_1_2N[0] - 1.5*(quant_1_2N[2] - quant_1_2N[0])
      
      bp_1 = plt.boxplot(Fuel_per_day_per_adult_2N,positions = [2], widths = 0.6)
      Fuel_D_A_2N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_2N):
          if a > Top_lim_1_2N or a < Low_lim_1_2N:
              Fuel_D_A_2N_outlier.append(f_d_a_2N[v])
              plt.text(2,a,f_d_a_2N[v])
      plt.text(2,0.1,'2N', color= 'g')
      #3N
      quant_1_3N = np.percentile(Fuel_per_day_per_adult_3N, [25,50,75])
      Top_lim_1_3N = quant_1_3N[2] + 1.5*(quant_1_3N[2] - quant_1_3N[0])
      Low_lim_1_3N = quant_1_3N[0] - 1.5*(quant_1_3N[2] - quant_1_3N[0])
      
      bp_1 = plt.boxplot(Fuel_per_day_per_adult_3N,positions = [3], widths = 0.6)
      count = 0
      Fuel_D_A_3N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_3N):
          if a > Top_lim_1_3N or a < Low_lim_1_3N:
              Fuel_D_A_3N_outlier.append(f_d_a_3N[v])
              count = count + 1
              if count == 2:
                  plt.text(3,a,f_d_a_3N[v],ha='left',va='bottom')
              elif count != 2:
                  plt.text(3,a,f_d_a_3N[v],ha='right',va='bottom')
      plt.text(3,0.1,'3N', color='r')        
      
      #4N
      quant_1_4N = np.percentile(Fuel_per_day_per_adult_4N, [25,50,75])
      Top_lim_1_4N = quant_1_4N[2] + 1.5*(quant_1_4N[2] - quant_1_4N[0])
      Low_lim_1_4N = quant_1_4N[0] - 1.5*(quant_1_4N[2] - quant_1_4N[0])
      
      bp_1 = plt.boxplot(Fuel_per_day_per_adult_4N,positions = [4], widths = 0.6)
      Fuel_D_A_4N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_4N):
          if a > Top_lim_1_4N or a < Low_lim_1_4N:
              Fuel_D_A_4N_outlier.append(f_d_a_4N[v])
              plt.text(4,a,f_d_a_4N[v])
      plt.text(4,0.1,'4N', color='y')        
              
      plt.xlim(0,5)
      plt.ylim(0,2.3)
      print('Fuel/Day/Adult 1N had these values as outliers   ', Fuel_D_A_1N_outlier)
      print('Fuel/Day/Adult 2N had these values as outliers   ', Fuel_D_A_2N_outlier)
      print('Fuel/Day/Adult 3N had these values as outliers   ', Fuel_D_A_3N_outlier)
      print('Fuel/Day/Adult 4N had these values as outliers   ', Fuel_D_A_4N_outlier)
      plt.show()
  
  
  
      # % change of fuel per day per adult between each phase
      fig_2, ax2 = plt.subplots()
      plt.title('% No_hood Change from Fuel per Day per Adult' )
      #plt.hold(True)
      #2N to 1N
      quant_1_2N_1N = np.percentile(Fuel_per_day_per_adult_2N_1N, [25,50,75])
      Top_lim_1_2N_1N = quant_1_2N_1N[2] + 1.5*(quant_1_2N_1N[2]-quant_1_2N_1N[0])
      Low_lim_1_2N_1N = quant_1_2N_1N[0] - 1.5*(quant_1_2N_1N[2]-quant_1_2N_1N[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_2N_1N, positions=[1], widths= 0.6)
      Fuel_D_A_2N_1N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_2N_1N):
          if a > Top_lim_1_2N_1N or a < Low_lim_1_2N_1N:
              Fuel_D_A_2N_1N_outlier.append(f_d_a_2N_1N[v])
              plt.text(1, a, f_d_a_2N_1N[v])
      plt.text(0.5, 0, '2N / 1N', color= 'g')
      
      #3N to 1N
      quant_1_3N_1N = np.percentile(Fuel_per_day_per_adult_3N_1N, [25,50,75])
      Top_lim_1_3N_1N = quant_1_3N_1N[2] + 1.5*(quant_1_3N_1N[2]-quant_1_3N_1N[0])
      Low_lim_1_3N_1N = quant_1_3N_1N[0] - 1.5*(quant_1_3N_1N[2]-quant_1_3N_1N[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_3N_1N, positions=[2], widths= 0.6)
      Fuel_D_A_3N_1N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_3N_1N):
          if a > Top_lim_1_3N_1N or a < Low_lim_1_3N_1N:
              Fuel_D_A_3N_1N_outlier.append(f_d_a_3N_1N[v])
              plt.text(2, a, f_d_a_3N_1N[v])
      plt.text(1.5, 0, '3N / 1N', color= 'r')
      
      #4N to 1N
      quant_1_4N_1N = np.percentile(Fuel_per_day_per_adult_4N_1N, [25,50,75])
      Top_lim_1_4N_1N = quant_1_4N_1N[2] + 1.5*(quant_1_4N_1N[2]-quant_1_4N_1N[0])
      Low_lim_1_4N_1N = quant_1_4N_1N[0] - 1.5*(quant_1_4N_1N[2]-quant_1_4N_1N[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_4N_1N, positions=[3], widths= 0.6)
      Fuel_D_A_4N_1N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_4N_1N):
          if a > Top_lim_1_4N_1N or a < Low_lim_1_4N_1N:
              Fuel_D_A_4N_1N_outlier.append(f_d_a_4N_1N[v])
              plt.text(3, a, f_d_a_4N_1N[v])
      plt.text(2.5, 0, '4N / 1N', color= 'y')
      
      #3N to 2N
      quant_1_3N_2N = np.percentile(Fuel_per_day_per_adult_3N_2N, [25,50,75])
      Top_lim_1_3N_2N = quant_1_3N_2N[2] + 1.5*(quant_1_3N_2N[2]-quant_1_3N_2N[0])
      Low_lim_1_3N_2N = quant_1_3N_2N[0] - 1.5*(quant_1_3N_2N[2]-quant_1_3N_2N[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_3N_2N, positions=[4], widths= 0.6)
      Fuel_D_A_3N_2N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_3N_2N):
          if a > Top_lim_1_3N_2N or a < Low_lim_1_3N_2N:
              Fuel_D_A_3N_2N_outlier.append(f_d_a_3N_2N[v])
              plt.text(4, a, f_d_a_3N_2N[v])
      plt.text(3.5, 0, '3N / 2N', color= 'm')
      
      #4N to 3N
      quant_1_4N_3N = np.percentile(Fuel_per_day_per_adult_4N_3N, [25,50,75])
      Top_lim_1_4N_3N = quant_1_4N_3N[2] + 1.5*(quant_1_4N_3N[2]-quant_1_4N_3N[0])
      Low_lim_1_4N_3N = quant_1_4N_3N[0] - 1.5*(quant_1_4N_3N[2]-quant_1_4N_3N[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_4N_3N, positions=[5], widths= 0.6)
      Fuel_D_A_4N_3N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_4N_3N):
          if a > Top_lim_1_4N_3N or a < Low_lim_1_4N_3N:
              Fuel_D_A_4N_3N_outlier.append(f_d_a_4N_3N[v])
              plt.text(5, a, f_d_a_4N_3N[v])
      plt.text(4.5, 0, '4N / 3N', color= 'k')
      
      #4N to 2N
      quant_1_4N_2N = np.percentile(Fuel_per_day_per_adult_4N_2N, [25,50,75])
      Top_lim_1_4N_2N = quant_1_4N_2N[2] + 1.5*(quant_1_4N_2N[2]-quant_1_4N_2N[0])
      Low_lim_1_4N_2N = quant_1_4N_2N[0] - 1.5*(quant_1_4N_2N[2]-quant_1_4N_2N[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_4N_2N, positions=[6], widths= 0.6)
      Fuel_D_A_4N_2N_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_4N_2N):
          if a > Top_lim_1_4N_2N or a < Low_lim_1_4N_2N:
              Fuel_D_A_4N_2N_outlier.append(f_d_a_4N_2N[v])
              plt.text(6, a, f_d_a_4N_2N[v])
      plt.text(5.5, 0, '4N / 2N', color= 'tab:orange')
      
      
      plt.xlim(0,7)
      plt.ylim(-0.5,4)
      print('Fuel/Day/Adult 2N/1N had these values as outliers   ', Fuel_D_A_2N_1N_outlier)
      print('Fuel/Day/Adult 3N/1N had these values as outliers   ', Fuel_D_A_3N_1N_outlier)
      print('Fuel/Day/Adult 4N/1N had these values as outliers   ', Fuel_D_A_4N_1N_outlier)
      print('Fuel/Day/Adult 3N/2N had these values as outliers   ', Fuel_D_A_3N_2N_outlier)
      print('Fuel/Day/Adult 4N/3N had these values as outliers   ', Fuel_D_A_4N_3N_outlier)
      print('Fuel/Day/Adult 4N/2N had these values as outliers   ', Fuel_D_A_4N_2N_outlier)
      plt.show()
      #adding averages to the tables
      quant_1_1N = np.append(quant_1_1N, np.average(Fuel_per_day_per_adult_1N))
      quant_1_2N = np.append(quant_1_2N, np.average(Fuel_per_day_per_adult_2N))
      quant_1_3N = np.append(quant_1_3N, np.average(Fuel_per_day_per_adult_3N))
      quant_1_4N = np.append(quant_1_4N, np.average(Fuel_per_day_per_adult_4N))
      
      D_50_quant_phase_f_d_a = {'Percentile %': ['25','50','75', 'Avg'], '1N': quant_1_1N, '2N': quant_1_2N,'3N' : quant_1_3N,'4N': quant_1_4N}
      F_D_A_50_phase_no_hood = pd.DataFrame(data=D_50_quant_phase_f_d_a, columns=['Percentile %','1N', '2N', '3N','4N'])
      
      quant_1_2N_1N = np.append(quant_1_2N_1N , np.average(Fuel_per_day_per_adult_2N_1N))
      quant_1_3N_1N = np.append(quant_1_3N_1N , np.average(Fuel_per_day_per_adult_3N_1N))
      quant_1_4N_1N = np.append(quant_1_4N_1N , np.average(Fuel_per_day_per_adult_4N_1N))
      quant_1_3N_2N = np.append(quant_1_3N_2N , np.average(Fuel_per_day_per_adult_3N_2N))
      quant_1_4N_3N = np.append(quant_1_4N_3N , np.average(Fuel_per_day_per_adult_4N_3N))
      quant_1_4N_2N = np.append(quant_1_4N_2N , np.average(Fuel_per_day_per_adult_4N_2N))
          
      D_50_quant_percent_f_d_a ={'Percentile %': ['25','50','75', 'Avg'],'2N / 1N': quant_1_2N_1N,'3N / 1N': quant_1_3N_1N,'4N / 1N': quant_1_4N_1N,
                                 '3N / 2N': quant_1_3N_2N,'4N / 3N': quant_1_4N_3N,'4N / 2N': quant_1_4N_2N}
      F_D_A_50_percent_change_no_hood = pd.DataFrame(data=D_50_quant_percent_f_d_a, columns=['Percentile %','2N / 1N','3N / 1N', '4N / 1N'
                                                                                             ,'3N / 2N','4N / 3N','4N / 2N'])
      print(F_D_A_50_phase_no_hood)
      print(F_D_A_50_percent_change_no_hood)
  # add more
  print ('-------------------Fuel per Day per Adult Hood Phase -------------------')
  
  if Hood_or_no == 'hood':
      Fuel_per_day_per_adult_1H = []
      f_d_a_1H = []
      Fuel_per_day_per_adult_2H = []
      f_d_a_2H = []
      Fuel_per_day_per_adult_3H = []
      f_d_a_3H = []
      
      count_t = 0
      count_f = 0
      for c in hood_counter:
          if c == (Household_removal[count_t] - C_Place_holder):
              count_t = count_t + 1
              if count_t == len(Household_removal):
                  count_t = 0
              continue
          if c == (Household_removal_Hood_fuel_day_adult[count_f] - C_Place_holder):
              count_f = count_f + 1
              if count_f == len(Household_removal_Hood_fuel_day_adult):
                  count_f = 0
              continue
          if Fuel_remove_1H_24hr.iloc[c,6] != -1.00:
              Fuel_per_day_per_adult_1H.append(Fuel_remove_1H_24hr.iloc[c,6]/Survey_1H.iloc[c,7])
              f_d_a_1H.append(Day_1H.iloc[c,0])
              
          if Fuel_remove_2H_24hr.iloc[c,6] != -1.00:
              Fuel_per_day_per_adult_2H.append(Fuel_remove_2H_24hr.iloc[c,6] / Survey_2H.iloc[c, 7])
              f_d_a_2H.append(Day_2H.iloc[c,0])
              
          if Fuel_remove_3H_24hr.iloc[c,6] != -1.00:
              Fuel_per_day_per_adult_3H.append(Fuel_remove_3H_24hr.iloc[c,6]/ Survey_3H.iloc[c, 7])
              f_d_a_3H.append(Day_3H.iloc[c, 0])
      
      # percentage Change of Fuel per day between the phases
      Fuel_per_day_per_adult_2H_1H = []
      f_d_a_2H_1H = []
      Fuel_per_day_per_adult_3H_1H = []
      f_d_a_3H_1H = []
      Fuel_per_day_per_adult_3H_2H = []
      f_d_a_3H_2H = []
      
      count_t = 0
      count_f = 0
      for c in hood_counter:
          if c == (Household_removal[count_t] - C_Place_holder):
              count_t = count_t + 1
              if count_t == len(Household_removal):
                  count_t = 0
              continue
          if c == (Household_removal_Hood_fuel_day_adult[count_f] - C_Place_holder):
              count_f = count_f + 1
              if count_f == len(Household_removal_Hood_fuel_day_adult):
                  count_f = 0
              continue
          if (len(Fuel_per_day_per_adult_2H)-1) >= c and (len(Fuel_per_day_per_adult_1H)-1) >= c:
              if Day_1H.iloc[c,13] > 0 and Day_2H.iloc[c,13]  > 0 and Day_1H.iloc[c,0] == Day_2H.iloc[c,0]:
                  Fuel_per_day_per_adult_2H_1H.append(Fuel_per_day_per_adult_2H[c]/Fuel_per_day_per_adult_1H[c])
                  f_d_a_2H_1H.append(Day_1H.iloc[c,0])
          if (len(Fuel_per_day_per_adult_3H)-1) >= c and (len(Fuel_per_day_per_adult_1H)-1) >= c:
              if Day_3H.iloc[c,13] > 0 and Day_1H.iloc[c,13]  > 0 and Day_3H.iloc[c,0] == Day_1H.iloc[c,0]:
                  Fuel_per_day_per_adult_3H_1H.append(Fuel_per_day_per_adult_3H[c]/Fuel_per_day_per_adult_1H[c])
                  f_d_a_3H_1H.append(Day_1H.iloc[c,0]) 
          if (len(Fuel_per_day_per_adult_3H)-1) >= c and (len(Fuel_per_day_per_adult_2H)-1) >= c:
              if Day_3H.iloc[c,13] > 0 and Day_2H.iloc[c,13]  > 0 and Day_3H.iloc[c,0] == Day_2H.iloc[c,0]:
                  Fuel_per_day_per_adult_3H_2H.append(Fuel_per_day_per_adult_3H[c]/Fuel_per_day_per_adult_2H[c])
                  f_d_a_3H_2H.append(Day_1H.iloc[c,0])
      
      # now for plotting
      #1H
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Fuel_per_day_per_adult_1H, ax=ax_box, color='b')
      sns.distplot(Fuel_per_day_per_adult_1H, ax=ax_hist, color='b')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('1H Fuel per Day per Adult')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      #2H
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Fuel_per_day_per_adult_2H, ax=ax_box, color='g')
      sns.distplot(Fuel_per_day_per_adult_2H, ax=ax_hist, color='g')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('2H Fuel per Day per Adult')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      #3H
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Fuel_per_day_per_adult_3H, ax=ax_box, color='r')
      sns.distplot(Fuel_per_day_per_adult_3H, ax=ax_hist, color='r')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('3H Fuel per Day per Adult')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      fig_2, ax_2 = plt.subplots()
      plt.title('Hood Fuel per Day per Adult')
      #plt.hold(True)
      
      quant_1_1H = np.percentile(Fuel_per_day_per_adult_1H, [25,50,75])
      Top_lim_1_1H = quant_1_1H[2] + 1.5*(quant_1_1H[2] - quant_1_1H[0])
      Low_lim_1_1H = quant_1_1H[0] - 1.5*(quant_1_1H[2] - quant_1_1H[0])
      
      bp_1 = plt.boxplot(Fuel_per_day_per_adult_1H, positions = [1], widths = 0.6)
      Fuel_D_A_1H_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_1H):
          if a > Top_lim_1_1H or a < Low_lim_1_1H:
              Fuel_D_A_1H_outlier.append(f_d_a_1H[v])
              plt.text(1,a,f_d_a_1H[v])
      plt.text(1,0,'1H',color='b')
          
          
      quant_1_2H = np.percentile(Fuel_per_day_per_adult_2H, [25,50,75])
      Top_lim_1_2H = quant_1_2H[2] + 1.5*(quant_1_2H[2] - quant_1_2H[0])
      Low_lim_1_2H = quant_1_2H[0] - 1.5*(quant_1_2H[2] - quant_1_2H[0])
      
      bp_1 = plt.boxplot(Fuel_per_day_per_adult_2H,positions = [2], widths = 0.6)
      count = 0
      Fuel_D_A_2H_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_2H):
          if a > Top_lim_1_2H or a < Low_lim_1_2H:
              Fuel_D_A_2H_outlier.append(f_d_a_2H[v])
              count = count + 1
              if count == 1:
                  plt.text(2,a,f_d_a_2H[v],ha='left',va='bottom')
              elif count !=1:
                  plt.text(2,a,f_d_a_2H[v],ha='right',va='bottom')
      plt.text(2,0,'2H', color= 'g')
      
      quant_1_3H = np.percentile(Fuel_per_day_per_adult_3H, [25,50,75])
      Top_lim_1_3H = quant_1_3H[2] + 1.5*(quant_1_3H[2] - quant_1_3H[0])
      Low_lim_1_3H = quant_1_3H[0] - 1.5*(quant_1_3H[2] - quant_1_3H[0])
      
      bp_1 = plt.boxplot(Fuel_per_day_per_adult_3H,positions = [3], widths = 0.6)
      count = 0
      Fuel_D_A_3H_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_3H):
          if a > Top_lim_1_3H or a < Low_lim_1_3H:
              Fuel_D_A_3H_outlier.append(f_d_a_3H[v])
              count = count + 1
              if count == 3:
                  plt.text(3,a,f_d_a_3H[v],ha='left',va='bottom')
              elif count != 1:
                  plt.text(3,a,f_d_a_3H[v],ha='right',va='bottom')
      plt.text(3,0,'3H', color='r')        
      
      
      plt.xlim(-0,4)
      plt.ylim(-0.25,2.5)
      print('Fuel/Day/Adult 1H had these values as outliers   ', Fuel_D_A_1H_outlier)
      print('Fuel/Day/Adult 2H had these values as outliers   ', Fuel_D_A_2H_outlier)
      print('Fuel/Day/Adult 3H had these values as outliers   ', Fuel_D_A_3H_outlier)
      plt.show()
      
      
      #% change of fuel perday per adult between each phase 
      fig_2, ax2 = plt.subplots()
      plt.title('% No_hood Change from Fuel per Day per Adult' )
      #plt.hold(True)
      #2H to 1H
      quant_1_2H_1H = np.percentile(Fuel_per_day_per_adult_2H_1H, [25,50,75])
      Top_lim_1_2H_1H = quant_1_2H_1H[2] + 1.5*(quant_1_2H_1H[2]-quant_1_2H_1H[0])
      Low_lim_1_2H_1H = quant_1_2H_1H[0] - 1.5*(quant_1_2H_1H[2]-quant_1_2H_1H[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_2H_1H, positions=[1], widths= 0.6)
      Fuel_D_A_2H_1H_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_2H_1H):
          if a > Top_lim_1_2H_1H or a < Low_lim_1_2H_1H:
              Fuel_D_A_2H_1H_outlier.append(f_d_a_2H_1H[v])
              plt.text(1, a, f_d_a_2H_1H[v])
      plt.text(0.75, -0.25, '2H / 1H', color= 'g')
      
      #3H to 1H
      quant_1_3H_1H = np.percentile(Fuel_per_day_per_adult_3H_1H, [25,50,75])
      Top_lim_1_3H_1H = quant_1_3H_1H[2] + 1.5*(quant_1_3H_1H[2]-quant_1_3H_1H[0])
      Low_lim_1_3H_1H = quant_1_3H_1H[0] - 1.5*(quant_1_3H_1H[2]-quant_1_3H_1H[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_3H_1H, positions=[2], widths= 0.6)
      Fuel_D_A_3H_1H_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_3H_1H):
          if a > Top_lim_1_3H_1H or a < Low_lim_1_3H_1H:
              Fuel_D_A_3H_1H_outlier.append(f_d_a_3H_1H[v])
              plt.text(2, a, f_d_a_3H_1H[v])
      plt.text(1.75, -0.25, '3H / 1H', color= 'r')
      
      #3H to 2H
      quant_1_3H_2H = np.percentile(Fuel_per_day_per_adult_3H_2H, [25,50,75])
      Top_lim_1_3H_2H = quant_1_3H_2H[2] + 1.5*(quant_1_3H_2H[2]-quant_1_3H_2H[0])
      Low_lim_1_3H_2H = quant_1_3H_2H[0] - 1.5*(quant_1_3H_2H[2]-quant_1_3H_2H[0])
  
      bp_1_1 = plt.boxplot(Fuel_per_day_per_adult_3H_2H, positions=[3], widths= 0.6)
      Fuel_D_A_3H_2H_outlier = []
      for v,a in enumerate(Fuel_per_day_per_adult_3H_2H):
          if a > Top_lim_1_3H_2H or a < Low_lim_1_3H_2H:
              Fuel_D_A_3H_2H_outlier.append(f_d_a_3H_2H[v])
              plt.text(3, a, f_d_a_3H_2H[v])
      plt.text(2.75, -0.25, '2H / 1H', color= 'm')
      
      plt.xlim(-0,4)
      plt.ylim(-0.25,6)
      print('Fuel/Day/Adult 2H/1H had these values as outliers   ', Fuel_D_A_2H_1H_outlier)
      print('Fuel/Day/Adult 3H/1H had these values as outliers   ', Fuel_D_A_3H_1H_outlier)
      print('Fuel/Day/Adult 3H/2H had these values as outliers   ', Fuel_D_A_3H_2H_outlier)
      plt.show()
      
      quant_1_1H = np.append(quant_1_1H, np.average(Fuel_per_day_per_adult_1H))
      quant_1_2H = np.append(quant_1_2H, np.average(Fuel_per_day_per_adult_2H))
      quant_1_3H = np.append(quant_1_3H, np.average(Fuel_per_day_per_adult_3H))
  
      
      D_50_quant_phase_f_d_a_hood = {'Percentile %': ['25','50','75', 'Avg'], '1H': quant_1_1H, '2H': quant_1_2H,'3H' : quant_1_3H}
      F_D_A_50_phase_hood = pd.DataFrame(data=D_50_quant_phase_f_d_a_hood, columns=['Percentile %','1H', '2H','3H'] )
      
      quant_1_2H_1H = np.append(quant_1_2H_1H , np.average(Fuel_per_day_per_adult_2H_1H))
      quant_1_3H_1H = np.append(quant_1_3H_1H , np.average(Fuel_per_day_per_adult_3H_1H))
      quant_1_3H_2H = np.append(quant_1_3H_2H , np.average(Fuel_per_day_per_adult_3H_2H))
      
      D_50_quant_percent_f_d_a_hood ={'Percentile %': ['25','50','75', 'Avg'],'2H / 1H': quant_1_2H_1H,'3H / 1H': quant_1_3H_1H,'3H / 2H': quant_1_3H_2H}
      F_D_A_50_percent_change_hood = pd.DataFrame(data=D_50_quant_percent_f_d_a_hood, columns=['Percentile %','2H / 1H','3H / 1H','3H / 2H'])
  
      print(F_D_A_50_phase_hood)
      print(F_D_A_50_percent_change_hood)
  print('----------------------- Kitchen PM per Day -----------------------------')
  if Hood_or_no == 'no_hood':
      Kit_PM_per_day_1N = []
      K_PM_D_1N = []
      Kit_PM_per_day_2N = []
      K_PM_D_2N = []
      Kit_PM_per_day_3N = []
      K_PM_D_3N = []
      Kit_PM_per_day_4N = []
      K_PM_D_4N = []
      count_t = 0
      count_pm = 0
      for c in NO_hood_counter:
          if c == (Household_removal[count_t] - C_Place_holder):
              count_t = count_t + 1
              if count_t == len(Household_removal):
                  count_t = 0
              continue
          if c == (Household_removal_NO_Hood_PM[count_pm] - C_Place_holder):
              count_pm = count_pm + 1
              if count_pm == len(Household_removal_NO_Hood_PM):
                  count_pm = 0
              continue
          # if Day_1N.iloc[c,7] != -1.00:
          #     Kit_PM_per_day_1N.append(Day_1N.iloc[c,7]/Day_1N.iloc[c,1])
          #     K_PM_D_1N.append(Day_1N.iloc[c,0])
          if Kit_PM_1N_24hr.iloc[c,6] != -1.00:
              Kit_PM_per_day_1N.append(Kit_PM_1N_24hr.iloc[c,6])
              K_PM_D_1N.append(Kit_PM_1N_24hr.iloc[c, 0])
          #if Day_2N.iloc[c, 7] != -1.00:
          #    Kit_PM_per_day_2N.append(Day_2N.iloc[c,7]/Day_2N.iloc[c,1])
          #    K_PM_D_2N.append(Day_2N.iloc[c,0])
          if Kit_PM_2N_24hr.iloc[c, 6] != -1.00:
              Kit_PM_per_day_2N.append(Kit_PM_2N_24hr.iloc[c, 6])
              K_PM_D_2N.append(Kit_PM_2N_24hr.iloc[c, 0])
          # if Day_3N.iloc[c, 7] != -1.00:
          #     Kit_PM_per_day_3N.append(Day_3N.iloc[c,7]/Day_3N.iloc[c,1])
          #     K_PM_D_3N.append(Day_3N.iloc[c, 0])
          if Kit_PM_3N_24hr.iloc[c, 6] != -1.00:
              Kit_PM_per_day_3N.append(Kit_PM_3N_24hr.iloc[c, 6])
              K_PM_D_3N.append(Kit_PM_3N_24hr.iloc[c, 0])
          # if Day_4N.iloc[c, 7] != -1.00:
          #     Kit_PM_per_day_4N.append(Day_4N.iloc[c,7]/Day_4N.iloc[c,1])
          #     K_PM_D_4N.append(Day_4N.iloc[c, 0])
          if Kit_PM_4N_24hr.iloc[c, 6] != -1.00:
              Kit_PM_per_day_4N.append(Kit_PM_4N_24hr.iloc[c, 6])
              K_PM_D_4N.append(Kit_PM_4N_24hr.iloc[c, 0])
       
      # percentages Between Phases of kitchen PM per day
      Kit_per_day_2N_1N = []
      K_PM_D_2N_1N = []
      Kit_per_day_3N_1N = []
      K_PM_D_3N_1N = []
      Kit_per_day_4N_1N = []
      K_PM_D_4N_1N = []
      
      Kit_per_day_3N_2N = []
      K_PM_D_3N_2N = []
      Kit_per_day_4N_3N = []
      K_PM_D_4N_3N = []
      Kit_per_day_4N_2N = []
      K_PM_D_4N_2N = []
  
      count_t = 0
      count_pm = 0
      for c in NO_hood_counter:
          if c == (Household_removal[count_t] - C_Place_holder):
              count_t = count_t + 1
              if count_t == len(Household_removal):
                  count_t = 0
              continue
          if c == (Household_removal_NO_Hood_PM[count_pm] - C_Place_holder):
              count_pm = count_pm + 1
              if count_pm == len(Household_removal_NO_Hood_PM):
                  count_pm = 0
              continue
          if (len(Kit_PM_per_day_2N)-1) >= c and (len(Kit_PM_per_day_1N)-1) >= c:
              #if Day_1N.iloc[c,7] > 0 and Day_2N.iloc[c,7]  > 0 and Day_1N.iloc[c,0] == Day_2N.iloc[c,0]:
              if  Kit_PM_1N_24hr.iloc[c,6] > 0 and Kit_PM_2N_24hr.iloc[c,6]  > 0 and Kit_PM_1N_24hr.iloc[c,0] == Kit_PM_2N_24hr.iloc[c,0]:
                  Kit_per_day_2N_1N.append(Kit_PM_per_day_2N[c]/Kit_PM_per_day_1N[c])
                  K_PM_D_2N_1N.append(Day_1N.iloc[c,0])
          if (len(Kit_PM_per_day_3N)-1) >= c and (len(Kit_PM_per_day_1N)-1) >= c:
              #if Day_3N.iloc[c,7] > 0 and Day_1N.iloc[c,7]  > 0 and Day_3N.iloc[c,0] == Day_1N.iloc[c,0]:
              if Kit_PM_3N_24hr.iloc[c, 6] > 0 and Kit_PM_1N_24hr.iloc[c, 6] > 0 and Kit_PM_3N_24hr.iloc[c, 0] == \
                      Kit_PM_1N_24hr.iloc[c, 0]:
                  Kit_per_day_3N_1N.append(Kit_PM_per_day_3N[c]/Kit_PM_per_day_1N[c])
                  K_PM_D_3N_1N.append(Day_1N.iloc[c,0])
          if (len(Kit_PM_per_day_4N)-1) >= c and (len(Kit_PM_per_day_1N)-1) >= c:
              #if Day_4N.iloc[c,7] > 0 and Day_1N.iloc[c,7]  > 0 and Day_4N.iloc[c,0] == Day_1N.iloc[c,0]:
              if Kit_PM_4N_24hr.iloc[c, 6] > 0 and Kit_PM_1N_24hr.iloc[c, 6] > 0 and Kit_PM_4N_24hr.iloc[c, 0] == \
                      Kit_PM_1N_24hr.iloc[c, 0]:
                  Kit_per_day_4N_1N.append(Kit_PM_per_day_4N[c]/Kit_PM_per_day_1N[c])
                  K_PM_D_4N_1N.append(Day_1N.iloc[c,0])
          if (len(Kit_PM_per_day_3N)-1) >= c and (len(Kit_PM_per_day_2N)-1) >= c:
              #if Day_3N.iloc[c,7] > 0 and Day_2N.iloc[c,7]  > 0 and Day_3N.iloc[c,0] == Day_2N.iloc[c,0]:
              if Kit_PM_3N_24hr.iloc[c, 6] > 0 and Kit_PM_2N_24hr.iloc[c, 6] > 0 and Kit_PM_3N_24hr.iloc[c, 0] == \
                      Kit_PM_2N_24hr.iloc[c, 0]:
                  Kit_per_day_3N_2N.append(Kit_PM_per_day_3N[c]/Kit_PM_per_day_2N[c])
                  K_PM_D_3N_2N.append(Day_2N.iloc[c,0])
          if (len(Kit_PM_per_day_4N)-1) >= c and (len(Kit_PM_per_day_3N)-1) >= c:
              #if Day_4N.iloc[c,7] > 0 and Day_3N.iloc[c,7]  > 0 and Day_4N.iloc[c,0] == Day_3N.iloc[c,0]:
              if Kit_PM_4N_24hr.iloc[c, 6] > 0 and Kit_PM_3N_24hr.iloc[c, 6] > 0 and Kit_PM_3N_24hr.iloc[c, 0] == \
                      Kit_PM_4N_24hr.iloc[c, 0]:
                  Kit_per_day_4N_3N.append(Kit_PM_per_day_4N[c]/Kit_PM_per_day_3N[c])
                  K_PM_D_4N_3N.append(Day_3N.iloc[c,0])
          if (len(Kit_PM_per_day_4N)-1) >= c and (len(Kit_PM_per_day_2N)-1) >= c:
              #if Day_4N.iloc[c,7] > 0 and Day_2N.iloc[c,7]  > 0 and Day_4N.iloc[c,0] == Day_2N.iloc[c,0]:
              if Kit_PM_4N_24hr.iloc[c, 6] > 0 and Kit_PM_4N_24hr.iloc[c, 6] > 0 and Kit_PM_4N_24hr.iloc[c, 0] == \
                      Kit_PM_2N_24hr.iloc[c, 0]:
                  Kit_per_day_4N_2N.append(Kit_PM_per_day_4N[c]/Kit_PM_per_day_2N[c])
                  K_PM_D_4N_2N.append(Day_4N.iloc[c,0])
        
      # now for box plotting for Kitchen PM per day percent changes
      
      #2N to 1N
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_2N_1N, ax=ax_box, color='g')
      sns.distplot(Kit_per_day_2N_1N, ax=ax_hist, color='g')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 2N/1N (Kitchen PM per Day)')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      #3N to 1N  
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_3N_1N, ax=ax_box, color='r')
      sns.distplot(Kit_per_day_3N_1N, ax=ax_hist, color='r')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 3N/1N (Kitchen PM per Day)')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      #4N to 1N
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_4N_1N, ax=ax_box, color='y')
      sns.distplot(Kit_per_day_4N_1N, ax=ax_hist, color='y')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 4N/1N (Kitchen PM per Day)')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      #3N to 2N 
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_3N_2N, ax=ax_box, color='m')
      sns.distplot(Kit_per_day_3N_2N, ax=ax_hist, color='m')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 3N/2N (Kitchen PM per Day)')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      #4N to 3N 
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_4N_3N, ax=ax_box, color='k')
      sns.distplot(Kit_per_day_4N_3N, ax=ax_hist, color='k')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 4N/3N (Kitchen PM per Day)')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      #4N to 2N 
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_4N_2N, ax=ax_box, color='tab:orange')
      sns.distplot(Kit_per_day_4N_2N, ax=ax_hist, color='tab:orange')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 4N/2N (Kitchen PM per Day)')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      
      #Plotting on the same graph
      fig, ax = plt.subplots()
      plt.title('No-Hood Kitchen PM per day')
      #plt.hold()
      #1N
      quant_1_1N = np.percentile(Kit_PM_per_day_1N, [25,50,75])
      Top_lim_1_1N = quant_1_1N[2] + 1.5*(quant_1_1N[2] - quant_1_1N[0])
      Low_lim_1_1N = quant_1_1N[0] - 1.5*(quant_1_1N[2] - quant_1_1N[0])
      
      bp_1 = plt.boxplot(Kit_PM_per_day_1N, positions = [1], widths = 0.6)
      kitchen_pm_1N_outlier = []
      for v,a in enumerate(Kit_PM_per_day_1N):
          if a > Top_lim_1_1N or a < Low_lim_1_1N:
              kitchen_pm_1N_outlier.append(K_PM_D_1N[v])
              plt.text(1,a,K_PM_D_1N[v])
      plt.text(1,0.1,'1N',color='b')
  
      #2N    
      quant_1_2N = np.percentile(Kit_PM_per_day_2N, [25,50,75])
      Top_lim_1_2N = quant_1_2N[2] + 1.5*(quant_1_2N[2] - quant_1_2N[0])
      Low_lim_1_2N = quant_1_2N[0] - 1.5*(quant_1_2N[2] - quant_1_2N[0])
      
      bp_1 = plt.boxplot(Kit_PM_per_day_2N,positions = [2], widths = 0.6)
      kitchen_pm_2N_outlier = []
      for v,a in enumerate(Kit_PM_per_day_2N):
          if a > Top_lim_1_2N or a < Low_lim_1_2N:
              kitchen_pm_2N_outlier.append(K_PM_D_2N[v])
              plt.text(2,a,K_PM_D_2N[v])
      plt.text(2,0.1,'2N', color= 'g')
      #3N
      quant_1_3N = np.percentile(Kit_PM_per_day_3N, [25,50,75])
      Top_lim_1_3N = quant_1_3N[2] + 1.5*(quant_1_3N[2] - quant_1_3N[0])
      Low_lim_1_3N = quant_1_3N[0] - 1.5*(quant_1_3N[2] - quant_1_3N[0])
      kitchen_pm_3N_outlier = []
      bp_1 = plt.boxplot(Kit_PM_per_day_3N,positions = [3], widths = 0.6)
      count = 0
      for v,a in enumerate(Kit_PM_per_day_3N):
          if a > Top_lim_1_3N or a < Low_lim_1_3N:
              kitchen_pm_3N_outlier.append(K_PM_D_3N[v])
              count = count + 1
              if count == (3):
                  plt.text(3,a,K_PM_D_3N[v],ha='left', va='bottom')
              if count == (1):
                  plt.text(3,a,K_PM_D_3N[v],ha='left', va='top')
              else:
                  plt.text(3,a,K_PM_D_3N[v],ha='right', va='bottom')
      plt.text(3,0.1,'3N', color='r')        
      
      #4N
      quant_1_4N = np.percentile(Kit_PM_per_day_4N, [25,50,75])
      Top_lim_1_4N = quant_1_4N[2] + 1.5*(quant_1_4N[2] - quant_1_4N[0])
      Low_lim_1_4N = quant_1_4N[0] - 1.5*(quant_1_4N[2] - quant_1_4N[0])
      
      bp_1 = plt.boxplot(Kit_PM_per_day_4N,positions = [4], widths = 0.6)
      kitchen_pm_4N_outlier = []
      for v,a in enumerate(Kit_PM_per_day_4N):
          if a > Top_lim_1_4N or a < Low_lim_1_4N:
              kitchen_pm_4N_outlier.append(K_PM_D_4N[v])
              plt.text(4,a,K_PM_D_4N[v])
      plt.text(4,0.1,'4N', color='y')        
              
      plt.xlim(0,5)
      plt.ylim(0,1200)
      print('Kitchen PM 1N had these values as outliers   ', kitchen_pm_1N_outlier)
      print('Kitchen PM 2N had these values as outliers   ', kitchen_pm_2N_outlier)
      print('Kitchen PM 3N had these values as outliers   ', kitchen_pm_3N_outlier)
      print('Kitchen PM 4N had these values as outliers   ', kitchen_pm_4N_outlier)
      plt.show()
  
  
      # % change of PM per day 
  
      fig_2, ax2 = plt.subplots()
      plt.title('% No_hood PM per Day Change' )
      #plt.hold(True)
      #2N to 1N
      quant_1_2N_1N = np.percentile(Kit_per_day_2N_1N, [25,50,75])
      Top_lim_1_2N_1N = quant_1_2N_1N[2] + 1.5*(quant_1_2N_1N[2]-quant_1_2N_1N[0])
      Low_lim_1_2N_1N = quant_1_2N_1N[0] - 1.5*(quant_1_2N_1N[2]-quant_1_2N_1N[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_2N_1N, positions=[1], widths= 0.6)
      kitchen_pm_2N_1N_outlier = []
      for v,a in enumerate(Kit_per_day_2N_1N):
          if a > Top_lim_1_2N_1N or a < Low_lim_1_2N_1N:
              kitchen_pm_2N_1N_outlier.append(K_PM_D_2N_1N[v])
              plt.text(1, a, K_PM_D_2N_1N[v])
      plt.text(0.5, -0.25, '2N / 1N', color= 'g')
      
      #3N to 1N
      quant_1_3N_1N = np.percentile(Kit_per_day_3N_1N, [25,50,75])
      Top_lim_1_3N_1N = quant_1_3N_1N[2] + 1.5*(quant_1_3N_1N[2]-quant_1_3N_1N[0])
      Low_lim_1_3N_1N = quant_1_3N_1N[0] - 1.5*(quant_1_3N_1N[2]-quant_1_3N_1N[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_3N_1N, positions=[2], widths= 0.6)
      kitchen_pm_3N_1N_outlier = []
      for v,a in enumerate(Kit_per_day_3N_1N):
          if a > Top_lim_1_3N_1N or a < Low_lim_1_3N_1N:
              kitchen_pm_3N_1N_outlier.append(K_PM_D_3N_1N[v])
              plt.text(2, a, K_PM_D_3N_1N[v])
      plt.text(1.5, -0.25, '3N / 1N', color= 'r')
      
      #4N to 1N
      quant_1_4N_1N = np.percentile(Kit_per_day_4N_1N, [25,50,75])
      Top_lim_1_4N_1N = quant_1_4N_1N[2] + 1.5*(quant_1_4N_1N[2]-quant_1_4N_1N[0])
      Low_lim_1_4N_1N = quant_1_4N_1N[0] - 1.5*(quant_1_4N_1N[2]-quant_1_4N_1N[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_4N_1N, positions=[3], widths= 0.6)
      kitchen_pm_4N_1N_outlier = []
      for v,a in enumerate(Kit_per_day_4N_1N):
          if a > Top_lim_1_4N_1N or a < Low_lim_1_4N_1N:
              kitchen_pm_4N_1N_outlier.append(K_PM_D_4N_1N[v])
              plt.text(3, a, K_PM_D_4N_1N[v])
      plt.text(2.5, -0.25, '4N / 1N', color= 'y')
      
      #3N to 2N
      quant_1_3N_2N = np.percentile(Kit_per_day_3N_2N, [25,50,75])
      Top_lim_1_3N_2N = quant_1_3N_2N[2] + 1.5*(quant_1_3N_2N[2]-quant_1_3N_2N[0])
      Low_lim_1_3N_2N = quant_1_3N_2N[0] - 1.5*(quant_1_3N_2N[2]-quant_1_3N_2N[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_3N_2N, positions=[4], widths= 0.6)
      kitchen_pm_3N_2N_outlier = []
      for v,a in enumerate(Kit_per_day_3N_2N):
          if a > Top_lim_1_3N_2N or a < Low_lim_1_3N_2N:
              kitchen_pm_3N_2N_outlier.append(K_PM_D_3N_2N[v])
              plt.text(4, a, K_PM_D_3N_2N[v])
      plt.text(3.5, -0.25, '3N / 2N', color= 'm')
      
      #4N to 3N
      quant_1_4N_3N = np.percentile(Kit_per_day_4N_3N, [25,50,75])
      Top_lim_1_4N_3N = quant_1_4N_3N[2] + 1.5*(quant_1_4N_3N[2]-quant_1_4N_3N[0])
      Low_lim_1_4N_3N = quant_1_4N_3N[0] - 1.5*(quant_1_4N_3N[2]-quant_1_4N_3N[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_4N_3N, positions=[5], widths= 0.6)
      kitchen_pm_4N_3N_outlier = []
      for v,a in enumerate(Kit_per_day_4N_3N):
          if a > Top_lim_1_4N_3N or a < Low_lim_1_4N_3N:
              kitchen_pm_4N_3N_outlier.append(K_PM_D_4N_3N[v])
              plt.text(5, a, K_PM_D_4N_3N[v])
      plt.text(4.5, -0.25, '4N / 3N', color= 'k')
      
      #4N to 2N
      quant_1_4N_2N = np.percentile(Kit_per_day_4N_2N, [25,50,75])
      Top_lim_1_4N_2N = quant_1_4N_2N[2] + 1.5*(quant_1_4N_2N[2]-quant_1_4N_2N[0])
      Low_lim_1_4N_2N = quant_1_4N_2N[0] - 1.5*(quant_1_4N_2N[2]-quant_1_4N_2N[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_4N_2N, positions=[6], widths= 0.6)
      kitchen_pm_4N_2N_outlier = []
      for v,a in enumerate(Kit_per_day_4N_2N):
          if a > Top_lim_1_4N_2N or a < Low_lim_1_4N_2N:
              kitchen_pm_4N_2N_outlier.append(K_PM_D_4N_2N[v])
              plt.text(6, a, K_PM_D_4N_2N[v])
      plt.text(5.5, -0.25, '4N / 2N', color= 'tab:orange')
      
  
      plt.xlim(0,7)
      plt.ylim(-0.5,5)
  
      print('Kitchen PM 2N/1N had these values as outliers   ', kitchen_pm_2N_1N_outlier)
      print('Kitchen PM 3N/1N had these values as outliers   ', kitchen_pm_3N_1N_outlier)
      print('Kitchen PM 4N/1N had these values as outliers   ', kitchen_pm_4N_1N_outlier)
      print('Kitchen PM 3N/2N had these values as outliers   ', kitchen_pm_3N_2N_outlier)
      print('Kitchen PM 4N/3N had these values as outliers   ', kitchen_pm_4N_3N_outlier)
      print('Kitchen PM 4N/2N had these values as outliers   ', kitchen_pm_4N_2N_outlier)
      plt.show()
      
      #adding averages to the tables
      quant_1_1N = np.append(quant_1_1N, np.average(Kit_PM_per_day_1N))
      quant_1_2N = np.append(quant_1_2N, np.average(Kit_PM_per_day_2N))
      quant_1_3N = np.append(quant_1_3N, np.average(Kit_PM_per_day_3N))
      quant_1_4N = np.append(quant_1_4N, np.average(Kit_PM_per_day_4N))
      
      D_50_quant_phase_PM_d = {'Percentile %': ['25','50','75', 'Avg'], '1N': quant_1_1N, '2N': quant_1_2N,'3N' : quant_1_3N,'4N': quant_1_4N}
      PM_D_50_phase_no_hood = pd.DataFrame(data=D_50_quant_phase_PM_d,columns=['Percentile %','1N', '2N', '3N','4N'])
      
      quant_1_2N_1N = np.append(quant_1_2N_1N , np.average(Kit_per_day_2N_1N))
      quant_1_3N_1N = np.append(quant_1_3N_1N , np.average(Kit_per_day_3N_1N))
      quant_1_4N_1N = np.append(quant_1_4N_1N , np.average(Kit_per_day_4N_1N))
      quant_1_3N_2N = np.append(quant_1_3N_2N , np.average(Kit_per_day_3N_2N))
      quant_1_4N_3N = np.append(quant_1_4N_3N , np.average(Kit_per_day_4N_3N))
      quant_1_4N_2N = np.append(quant_1_4N_2N , np.average(Kit_per_day_4N_2N))
      
      D_50_quant_percent_PM_d ={'Percentile %': ['25','50','75', 'Avg'],'2N / 1N': quant_1_2N_1N,'3N / 1N': quant_1_3N_1N,'4N / 1N': quant_1_4N_1N,
                                 '3N / 2N': quant_1_3N_2N,'4N / 3N': quant_1_4N_3N,'4N / 2N': quant_1_4N_2N}
      PM_D_50_percent_change_no_hood = pd.DataFrame(data=D_50_quant_percent_PM_d, columns=['Percentile %','2N / 1N','3N / 1N', '4N / 1N'
                                                                                             ,'3N / 2N','4N / 3N','4N / 2N'])
  
      
      print(PM_D_50_phase_no_hood)
      print(PM_D_50_percent_change_no_hood)
      
  # hood Pm per day
  if Hood_or_no == 'hood':
      Kit_PM_per_day_1H = []
      K_PM_D_1H = []
      Kit_PM_per_day_2H = []
      K_PM_D_2H = []
      Kit_PM_per_day_3H = []
      K_PM_D_3H = []
  
      count_t = 0
      count_pm = 0
      for c in hood_counter:
          if c == (Household_removal[count_t] - C_Place_holder):
              count_t = count_t + 1
              if count_t == len(Household_removal):
                  count_t = 0
              continue
          if c == (Household_removal_Hood_PM[count_pm] - C_Place_holder):
              count_pm = count_pm + 1
              if count_pm == len(Household_removal_Hood_PM):
                  count_pm = 0
              continue
          # if Day_1H.iloc[c,7] != -1.00:
          #     Kit_PM_per_day_1H.append(Day_1H.iloc[c,7]/Day_1H.iloc[c,1])
          #     K_PM_D_1H.append(Day_1H.iloc[c,0])
          if Kit_PM_1H_24hr.iloc[c, 6] != -1.00:
              Kit_PM_per_day_1H.append(Kit_PM_1H_24hr.iloc[c,6])
              K_PM_D_1H.append(Kit_PM_1H_24hr.iloc[c,0])
          # if Day_2H.iloc[c, 7] != -1.00:
          #     Kit_PM_per_day_2H.append(Day_2H.iloc[c,7]/Day_2H.iloc[c,1])
          #     K_PM_D_2H.append(Day_2H.iloc[c,0])
          if Kit_PM_2H_24hr.iloc[c, 6] != -1.00:
              Kit_PM_per_day_2H.append(Kit_PM_2H_24hr.iloc[c,6])
              K_PM_D_2H.append(Kit_PM_2H_24hr.iloc[c,0])
          # if Day_3H.iloc[c, 7] != -1.00:
          #     Kit_PM_per_day_3H.append(Day_3H.iloc[c,7]/Day_3H.iloc[c,1])
          #     K_PM_D_3H.append(Day_3H.iloc[c, 0])
          if Kit_PM_3H_24hr.iloc[c, 6] != -1.00:
              Kit_PM_per_day_3H.append(Kit_PM_3H_24hr.iloc[c,6])
              K_PM_D_3H.append(Kit_PM_3H_24hr.iloc[c,0])
              
       
      # percentages Between Phases of kitchen PM per day
      Kit_per_day_2H_1H = []
      K_PM_D_2H_1H = []
      Kit_per_day_3H_1H = []
      K_PM_D_3H_1H = []
      Kit_per_day_3H_2H = []
      K_PM_D_3H_2H = []
  
  
      count_t = 0
      count_pm = 0
      for c in NO_hood_counter:
          if c == (Household_removal[count_t] - C_Place_holder):
              count_t = count_t + 1
              if count_t == len(Household_removal):
                  count_t = 0
              continue
          if c == (Household_removal_Hood_PM[count_pm] - C_Place_holder):
              count_pm = count_pm + 1
              if count_pm == len(Household_removal_Hood_PM):
                  count_pm = 0
              continue
          if (len(Kit_PM_per_day_2H)-1) >= c and (len(Kit_PM_per_day_1H)-1) >= c:
              #if Day_1H.iloc[c,7] > 0 and Day_2H.iloc[c,7]  > 0 and Day_1H.iloc[c,0] == Day_2H.iloc[c,0]:
              if Kit_PM_1H_24hr.iloc[c, 6] > 0 and Kit_PM_2H_24hr.iloc[c, 6] > 0 and Kit_PM_1H_24hr.iloc[c, 0] == Kit_PM_2H_24hr.iloc[c, 0]:
                  Kit_per_day_2H_1H.append(Kit_PM_per_day_2H[c]/Kit_PM_per_day_1H[c])
                  K_PM_D_2H_1H.append(Day_1H.iloc[c,0])
          if (len(Kit_PM_per_day_3H)-1) >= c and (len(Kit_PM_per_day_1H)-1) >= c:
              #if Day_3H.iloc[c,7] > 0 and Day_1H.iloc[c,7]  > 0 and Day_3H.iloc[c,0] == Day_1H.iloc[c,0]:
              if Kit_PM_3H_24hr.iloc[c, 6] > 0 and Kit_PM_1H_24hr.iloc[c, 6] > 0 and Kit_PM_1H_24hr.iloc[c, 0] == \
                      Kit_PM_3H_24hr.iloc[c, 0]:
                  Kit_per_day_3H_1H.append(Kit_PM_per_day_3H[c]/Kit_PM_per_day_1H[c])
                  K_PM_D_3H_1H.append(Day_1H.iloc[c,0])
          if (len(Kit_PM_per_day_3H)-1) >= c and (len(Kit_PM_per_day_2H)-1) >= c:
              #if Day_3H.iloc[c,7] > 0 and Day_2H.iloc[c,7]  > 0 and Day_3H.iloc[c,0] == Day_2H.iloc[c,0]:
              if Kit_PM_3H_24hr.iloc[c, 6] > 0 and Kit_PM_2H_24hr.iloc[c, 6] > 0 and Kit_PM_3H_24hr.iloc[c, 0] == \
                      Kit_PM_2H_24hr.iloc[c, 0]:
                  Kit_per_day_3H_2H.append(Kit_PM_per_day_3H[c]/Kit_PM_per_day_2H[c])
                  K_PM_D_3H_2H.append(Day_2H.iloc[c,0])
  
        
      # now for box plotting for Kitchen PM per day percent changes
      
      #2H to 1H
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_2H_1H, ax=ax_box, color='g')
      sns.distplot(Kit_per_day_2H_1H, ax=ax_hist, color='g')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 2H/1H (Kitchen PM per Day)')
      plt.ylim(top=1.5)
      plt.ylim(bottom = 0)
      
      #3H to 1H  
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_3H_1H, ax=ax_box, color='r')
      sns.distplot(Kit_per_day_3H_1H, ax=ax_hist, color='r')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 3H/1H (Kitchen PM per Day)')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      #3H to 2H 
      sns.set(style="ticks")
      f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={"height_ratios": (0.15, 0.85)})
      sns.boxplot(Kit_per_day_3H_2H, ax=ax_box, color='m')
      sns.distplot(Kit_per_day_3H_2H, ax=ax_hist, color='m')
      ax_box.set(yticks=[])
      sns.despine(ax=ax_hist)
      sns.despine(ax=ax_box, left=True)
      plt.title('% 3H/2H (Kitchen PM per Day)')
      plt.ylim(top=2)
      plt.ylim(bottom = 0)
      
      
      #Plotting on the same graph
      fig, ax = plt.subplots()
      plt.title('Hood Kitchen PM per day')
      #1H
      quant_1_1H = np.percentile(Kit_PM_per_day_1H, [25,50,75])
      Top_lim_1_1H = quant_1_1H[2] + 1.5*(quant_1_1H[2] - quant_1_1H[0])
      Low_lim_1_1H = quant_1_1H[0] - 1.5*(quant_1_1H[2] - quant_1_1H[0])
      
      bp_1 = plt.boxplot(Kit_PM_per_day_1H, positions = [1], widths = 0.6)
      kitchen_pm_1H_outlier = []
      for v,a in enumerate(Kit_PM_per_day_1H):
          if a > Top_lim_1_1H or a < Low_lim_1_1H:
              kitchen_pm_1H_outlier.append(K_PM_D_1H[v])
              plt.text(1,a,K_PM_D_1H[v])
      plt.text(0.5,0.1,'1H',color='b')
  
      #2N    
      quant_1_2H = np.percentile(Kit_PM_per_day_2H, [25,50,75])
      Top_lim_1_2N = quant_1_2H[2] + 1.5*(quant_1_2H[2] - quant_1_2H[0])
      Low_lim_1_2N = quant_1_2H[0] - 1.5*(quant_1_2H[2] - quant_1_2H[0])
      
      bp_1 = plt.boxplot(Kit_PM_per_day_2H,positions = [2], widths = 0.6)
      kitchen_pm_2H_outlier = []
      for v,a in enumerate(Kit_PM_per_day_2H):
          if a > Top_lim_1_2N or a < Low_lim_1_2N:
              kitchen_pm_2H_outlier.append(K_PM_D_2H[v])
              plt.text(2,a,K_PM_D_2H[v])
      plt.text(1.5,0.1,'2H', color= 'g')
      #3H
      quant_1_3H = np.percentile(Kit_PM_per_day_3H, [25,50,75])
      Top_lim_1_3N = quant_1_3H[2] + 1.5*(quant_1_3H[2] - quant_1_3H[0])
      Low_lim_1_3N = quant_1_3H[0] - 1.5*(quant_1_3H[2] - quant_1_3H[0])
      kitchen_3H_outlier = []
      bp_1 = plt.boxplot(Kit_PM_per_day_3H,positions = [3], widths = 0.6)
      count = 0
      kitchen_pm_3H_outlier = []
      for v,a in enumerate(Kit_PM_per_day_3H):
          if a > Top_lim_1_3N or a < Low_lim_1_3N:
              kitchen_pm_3H_outlier.append(K_PM_D_3H[v])
              plt.text(3,a,K_PM_D_3H[v])
  #            kitchen_3N_outlier.append(K_PM_D_3N[v])
  #            count = count + 1
  #            if count == (3):
  #                plt.text(3,a,K_PM_D_3N[v],ha='left', va='bottom')
  #            if count == (1):
  #                plt.text(3,a,K_PM_D_3N[v],ha='left', va='top')
  #            else:
  #                plt.text(3,a,K_PM_D_3N[v],ha='right', va='bottom')
      plt.text(2.5,0.1,'3H', color='r')        
      plt.xlim(0,4)
      plt.ylim(0,1200)
      print('Kitchen PM 1H had these values as outliers   ', kitchen_pm_1H_outlier)
      print('Kitchen PM 2H had these values as outliers   ', kitchen_pm_2H_outlier)
      print('Kitchen PM 3H had these values as outliers   ', kitchen_pm_3H_outlier)
      plt.show()
      #print('3N had these values as outliers   ' , kitchen_3N_outlier)
  
      # % change of PM per day 
  
      fig_2, ax2 = plt.subplots()
      plt.title('% hood PM per Day Change' )
      #plt.hold(True)
      #2H to 1H
      quant_1_2H_1H = np.percentile(Kit_per_day_2H_1H, [25,50,75])
      Top_lim_1_2N_1N = quant_1_2H_1H[2] + 1.5*(quant_1_2H_1H[2]-quant_1_2H_1H[0])
      Low_lim_1_2N_1N = quant_1_2H_1H[0] - 1.5*(quant_1_2H_1H[2]-quant_1_2H_1H[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_2H_1H, positions=[1], widths= 0.6)
      kitchen_pm_2H_1H_outlier = []
      for v,a in enumerate(Kit_per_day_2H_1H):
          if a > Top_lim_1_2N_1N or a < Low_lim_1_2N_1N:
              kitchen_pm_2H_1H_outlier.append(K_PM_D_2H_1H[v])
              plt.text(1, a, K_PM_D_2H_1H[v])
      plt.text(0.75, -0.25, '2H / 1H', color= 'g')
      
      #3H to 1H
      quant_1_3H_1H = np.percentile(Kit_per_day_3H_1H, [25,50,75])
      Top_lim_1_3N_1N = quant_1_3H_1H[2] + 1.5*(quant_1_3H_1H[2]-quant_1_3H_1H[0])
      Low_lim_1_3N_1N = quant_1_3H_1H[0] - 1.5*(quant_1_3H_1H[2]-quant_1_3H_1H[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_3H_1H, positions=[2], widths= 0.6)
      kitchen_pm_3H_1H_outlier = []
      for v,a in enumerate(Kit_per_day_3H_1H):
          if a > Top_lim_1_3N_1N or a < Low_lim_1_3N_1N:
              kitchen_pm_3H_1H_outlier.append(K_PM_D_3H_1H[v])
              plt.text(2, a, K_PM_D_3H_1H[v])
      plt.text(1.75, -0.25, '3H / 1H', color= 'r')
  
      #3H to 2H
      quant_1_3H_2H = np.percentile(Kit_per_day_3H_2H, [25,50,75])
      Top_lim_1_3N_2N = quant_1_3H_2H[2] + 1.5*(quant_1_3H_2H[2]-quant_1_3H_2H[0])
      Low_lim_1_3N_2N = quant_1_3H_2H[0] - 1.5*(quant_1_3H_2H[2]-quant_1_3H_2H[0])
  
      bp_1_1 = plt.boxplot(Kit_per_day_3H_2H, positions=[3], widths= 0.6)
      kitchen_pm_3H_2H_outlier = []
      for v,a in enumerate(Kit_per_day_3H_2H):
          if a > Top_lim_1_3N_2N or a < Low_lim_1_3N_2N:
              kitchen_pm_3H_2H_outlier.append(K_PM_D_3H_2H[v])
              plt.text(3, a, K_PM_D_3H_2H[v])
      plt.text(2.75, -0.25, '3H / 2H', color= 'm')
  
      plt.xlim(0,4)
      plt.ylim(-0.5,5)
      print('Kitchen PM 2H/1H had these values as outliers   ', kitchen_pm_2H_1H_outlier)
      print('Kitchen PM 3H/1H had these values as outliers   ', kitchen_pm_3H_1H_outlier)
      print('Kitchen PM 3H/2H had these values as outliers   ', kitchen_pm_3H_2H_outlier)
      plt.show()
      
      quant_1_1H = np.append(quant_1_1H, np.average(Kit_PM_per_day_1H))
      quant_1_2H = np.append(quant_1_2H, np.average(Kit_PM_per_day_2H))
      quant_1_3H = np.append(quant_1_3H, np.average(Kit_PM_per_day_3H))
      
      D_50_quant_phase_PM_D_hood = {'Percentile %':  ['25','50','75', 'Avg'], '1H': quant_1_1H, '2H': quant_1_2H,'3H' : quant_1_3H}
      PM_D_50_phase_hood = pd.DataFrame(data=D_50_quant_phase_PM_D_hood, columns= ['Percentile %','1H','2H','3H' ])
      
      quant_1_2H_1H = np.append(quant_1_2H_1H , np.average(Kit_per_day_2H_1H))
      quant_1_3H_1H = np.append(quant_1_3H_1H , np.average(Kit_per_day_3H_1H))
      quant_1_3H_2H = np.append(quant_1_3H_2H , np.average(Kit_per_day_3H_2H))
      
      
      D_50_quant_percent_PM_D_hood ={'Percentile %':  ['25','50','75', 'Avg'],'2H / 1H': quant_1_2H_1H,'3H / 1H': quant_1_3H_1H,'3H / 2H': quant_1_3H_2H}
      PM_D_50_percent_change_hood = pd.DataFrame(data=D_50_quant_percent_PM_D_hood, columns=['Percentile %','2H / 1H','3H / 1H','3H / 2H'])
  
      print(PM_D_50_phase_hood)
      print(PM_D_50_percent_change_hood)
  # when i am ready to transfer to a data frame and get the differences
  
  #histograms for the comparison
  if Hood_or_no == 'no_hood':
      plt.title('Histogram of Fuel per 24 Hours per Person - No Hood' )
      plt.hist([Fuel_per_day_per_adult_1N],
              color=['b'], alpha=0.5, label='1N')
      plt.hist([Fuel_per_day_per_adult_2N],
               color=['g'], alpha=0.5, label='2N')
      plt.hist([Fuel_per_day_per_adult_3N],
               color=['r'], alpha=0.5, label='3N')
      plt.hist([Fuel_per_day_per_adult_4N],
               color=['y'], alpha=0.5, label='4N')
      plt.legend(loc='upper right')
      plt.show()
  
  
      plt.title('Histogram of Kitchen PM 24 Hours - No Hood' )
      plt.hist([Kit_PM_per_day_1N],
              color=['b'], alpha=0.5, label='1N')
      plt.hist([Kit_PM_per_day_2N],
               color=['g'], alpha=0.5, label='2N')
      plt.hist([Kit_PM_per_day_3N],
               color=['r'], alpha=0.5, label='3N')
      plt.hist([Kit_PM_per_day_4N],
               color=['y'], alpha=0.5, label='4N')
      plt.legend(loc='upper right')
      plt.show()
  
  if Hood_or_no == 'hood':
      plt.title('Histogram of Fuel per 24 Hours per Person - Hood' )
      plt.hist([Fuel_per_day_per_adult_1H],
              color=['b'], alpha=0.5, label='1H')
      plt.hist([Fuel_per_day_per_adult_2H],
               color=['g'], alpha=0.5, label='2H')
      plt.hist([Fuel_per_day_per_adult_3H],
               color=['r'], alpha=0.5, label='3H')
      plt.legend(loc='upper right')
      plt.show()
  
      plt.title('Histogram of Kitchen PM 24 Hours - Hood' )
      plt.hist([Kit_PM_per_day_1H],
              color=['b'], alpha=0.5, label='1H')
      plt.hist([Kit_PM_per_day_2H],
               color=['g'], alpha=0.5, label='2H')
      plt.hist([Kit_PM_per_day_3H],
               color=['r'], alpha=0.5, label='3H')
      plt.legend(loc='upper right')
      plt.show()
  
apis: matplotlib.pyplot.boxplot matplotlib.pyplot.legend pandas.read_csv pandas.DataFrame matplotlib.pyplot.subplots pandas.set_option numpy.arange matplotlib.pyplot.title matplotlib.pyplot.xlim matplotlib.pyplot.show matplotlib.pyplot.text matplotlib.pyplot.ylim matplotlib.pyplot.hist numpy.average numpy.percentile