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script.py

# names of hurricanes
names = ['Cuba I', 'San Felipe II Okeechobee', 'Bahamas', 'Cuba II', 'CubaBrownsville', 'Tampico', 'Labor Day', 'New England', 'Carol', 'Janet', 'Carla', 'Hattie', 'Beulah', 'Camille', 'Edith', 'Anita', 'David', 'Allen', 'Gilbert', 'Hugo', 'Andrew', 'Mitch', 'Isabel', 'Ivan', 'Emily', 'Katrina', 'Rita', 'Wilma', 'Dean', 'Felix', 'Matthew', 'Irma', 'Maria', 'Michael']

# months of hurricanes
months = ['October', 'September', 'September', 'November', 'August', 'September', 'September', 'September', 'September', 'September', 'September', 'October', 'September', 'August', 'September', 'September', 'August', 'August', 'September', 'September', 'August', 'October', 'September', 'September', 'July', 'August', 'September', 'October', 'August', 'September', 'October', 'September', 'September', 'October']

# years of hurricanes
years = [1924, 1928, 1932, 1932, 1933, 1933, 1935, 1938, 1953, 1955, 1961, 1961, 1967, 1969, 1971, 1977, 1979, 1980, 1988, 1989, 1992, 1998, 2003, 2004, 2005, 2005, 2005, 2005, 2007, 2007, 2016, 2017, 2017, 2018]

# maximum sustained winds (mph) of hurricanes
max_sustained_winds = [165, 160, 160, 175, 160, 160, 185, 160, 160, 175, 175, 160, 160, 175, 160, 175, 175, 190, 185, 160, 175, 180, 165, 165, 160, 175, 180, 185, 175, 175, 165, 180, 175, 160]

# areas affected by each hurricane
areas_affected = [['Central America', 'Mexico', 'Cuba', 'Florida', 'The Bahamas'], ['Lesser Antilles', 'The Bahamas', 'United States East Coast', 'Atlantic Canada'], ['The Bahamas', 'Northeastern United States'], ['Lesser Antilles', 'Jamaica', 'Cayman Islands', 'Cuba', 'The Bahamas', 'Bermuda'], ['The Bahamas', 'Cuba', 'Florida', 'Texas', 'Tamaulipas'], ['Jamaica', 'Yucatn Peninsula'], ['The Bahamas', 'Florida', 'Georgia', 'The Carolinas', 'Virginia'], ['Southeastern United States', 'Northeastern United States', 'Southwestern Quebec'], ['Bermuda', 'New England', 'Atlantic Canada'], ['Lesser Antilles', 'Central America'], ['Texas', 'Louisiana', 'Midwestern United States'], ['Central America'], ['The Caribbean', 'Mexico', 'Texas'], ['Cuba', 'United States Gulf Coast'], ['The Caribbean', 'Central America', 'Mexico', 'United States Gulf Coast'], ['Mexico'], ['The Caribbean', 'United States East coast'], ['The Caribbean', 'Yucatn Peninsula', 'Mexico', 'South Texas'], ['Jamaica', 'Venezuela', 'Central America', 'Hispaniola', 'Mexico'], ['The Caribbean', 'United States East Coast'], ['The Bahamas', 'Florida', 'United States Gulf Coast'], ['Central America', 'Yucatn Peninsula', 'South Florida'], ['Greater Antilles', 'Bahamas', 'Eastern United States', 'Ontario'], ['The Caribbean', 'Venezuela', 'United States Gulf Coast'], ['Windward Islands', 'Jamaica', 'Mexico', 'Texas'], ['Bahamas', 'United States Gulf Coast'], ['Cuba', 'United States Gulf Coast'], ['Greater Antilles', 'Central America', 'Florida'], ['The Caribbean', 'Central America'], ['Nicaragua', 'Honduras'], ['Antilles', 'Venezuela', 'Colombia', 'United States East Coast', 'Atlantic Canada'], ['Cape Verde', 'The Caribbean', 'British Virgin Islands', 'U.S. Virgin Islands', 'Cuba', 'Florida'], ['Lesser Antilles', 'Virgin Islands', 'Puerto Rico', 'Dominican Republic', 'Turks and Caicos Islands'], ['Central America', 'United States Gulf Coast (especially Florida Panhandle)']]

# damages (USD($)) of hurricanes
damages = ['Damages not recorded', '100M', 'Damages not recorded', '40M', '27.9M', '5M', 'Damages not recorded', '306M', '2M', '65.8M', '326M', '60.3M', '208M', '1.42B', '25.4M', 'Damages not recorded', '1.54B', '1.24B', '7.1B', '10B', '26.5B', '6.2B', '5.37B', '23.3B', '1.01B', '125B', '12B', '29.4B', '1.76B', '720M', '15.1B', '64.8B', '91.6B', '25.1B']

# deaths for each hurricane
deaths = [90,4000,16,3103,179,184,408,682,5,1023,43,319,688,259,37,11,2068,269,318,107,65,19325,51,124,17,1836,125,87,45,133,603,138,3057,74]

# write your update damages function here:

def numberfy(dam):
  new = []
  dam = [i.replace('M', '000000') for i in dam]
  dam = [i.replace('B', '000000000')for i in dam]
  dam = [i.replace('.', '') for i in dam]
  for i in dam:
    if i != 'Damages not recorded':
      new.append(float(i))
    else:
      new.append(i)
  return new
damages = numberfy(damages)





# write your construct hurricane dictionary function here:
def construct_hurricane_dictionary(names, months, years, max_sustained_winds, areas_affected, damages, deaths):
  lst = []
  for i in range(len(names)):
    lst.append({'Name': names[i], 'Month': months[i], 'Year': years[i], 'Max Sustained Wind': max_sustained_winds[i], 'Areas Affected': areas_affected[i], 'Damage': damages[i], 'Deaths': deaths[i]})
  return lst

dictionary = construct_hurricane_dictionary(names, months, years, max_sustained_winds, areas_affected, damages, deaths)






# write your construct hurricane by year dictionary function here:
def construct_hurricane_by_year_dictionary(dictionary, year):
  lst = []
  for hurricane in dictionary:
    if hurricane['Year'] == year:
      lst.append(hurricane)
  return lst



# write your count affected areas function here:
def count_affected_areas(dictionary):
  dic = {}
  for hurricane in dictionary:
    for area in hurricane['Areas Affected']:
      if area in dic:
        dic[area] += 1
      else:
        dic[area] = 1
  return dic






# write your find most affected area function here:
def most_affected_area(dictionary):
  dic = count_affected_areas(dictionary)
  most_area = ''
  most_count = 0
  for area in dic:
    if dic[area] > most_count:
      most_area = area
      most_count = dic[area]
  return {most_area: most_count}

# write your greatest number of deaths function here:
def greatest_number_deaths(dictionary):
  deathly_hurricane = ''
  death_count = 0
  for hurricane in dictionary:
    if hurricane['Deaths'] > death_count:
      deathly_hurricane = hurricane['Name']
      death_count = hurricane['Deaths']
  return {deathly_hurricane: death_count}




# write your catgeorize by mortality function here:
mortality_scale = {0: 0,
                   1: 100,
                   2: 500,
                   3: 1000,
                   4: 10000}
def categorize_by_mortality(dictionary):
  lst = []
  for hurricane in dictionary:
    if hurricane['Deaths'] < 100:
      hurricane['Mortality Scale'] = 0
      lst.append(hurricane)
    elif hurricane['Deaths'] < 500:
      hurricane['Mortality Scale'] = 1
      lst.append(hurricane)
    elif hurricane['Deaths'] < 500:
      hurricane['Mortality Scale'] = 2
      lst.append(hurricane)
    elif hurricane['Deaths'] < 1000:
      hurricane['Mortality Scale'] = 3
      lst.append(hurricane)
    elif hurricane['Deaths'] < 10000:
      hurricane['Mortality Scale'] = 4
      lst.append(hurricane)
    else:
      hurricane['Mortality Scale'] = 5
      lst.append(hurricane)
  return lst

scale1 = categorize_by_mortality(dictionary)






# write your greatest damage function here:
def greatest_damage(dictionary):
  costly_hurricane = ''
  cost_count = 0
  for hurricane in dictionary:
    if hurricane['Damage'] == 'Damages not recorded':
      pass
    elif hurricane['Damage'] > cost_count:
      costly_hurricane = hurricane['Name']
      cost_count = hurricane['Damage']
  return {costly_hurricane: cost_count}


print(greatest_damage(dictionary))




# write your catgeorize by damage function here:
def categorize_by_damage(dictionary):
  lst = []
  for hurricane in dictionary:
    if hurricane['Damage'] == 'Damages not recorded':
      pass
    elif (hurricane['Damage']) < 100000000:
      hurricane['Damage Scale'] = 0
      lst.append(hurricane)
    elif hurricane['Damage'] < 1000000000:
      hurricane['Damage Scale'] = 1
      lst.append(hurricane)
    elif hurricane['Damage'] < 10000000000:
      hurricane['Damage Scale'] = 2
      lst.append(hurricane)
    elif hurricane['Damage'] < 100000000000:
      hurricane['Damage Scale'] = 3
      lst.append(hurricane)
    elif hurricane['Damage'] < 1000000000000:
      hurricane['Damage Scale'] = 4
      lst.append(hurricane)
    else:
      hurricane['Damage Scale'] = 5
      lst.append(hurricane)
  return lst

scaled_dictionary = categorize_by_damage(scale1)


print(scaled_dictionary)