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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]

# 2
# Update Recorded Damages
conversion = {"M": 1000000,
              "B": 1000000000}
def updated_damages(dmg_list):
  updated_list = []
  for ele in dmg_list:
    if ele == 'Damages not recorded':
      updated_list.append(ele)
    elif ele[-1] == "M":
      new_ele = float(ele[:-1]) * conversion.get("M")
      updated_list.append(new_ele)
    else: 
      new_ele = float(ele[:-1]) * conversion.get("B")
      updated_list.append(new_ele)
  #print(updated_list)
  return updated_list

# test function by updating damages
updated_damage = updated_damages(damages)

#3
# Create and view the hurricanes dictionary
def dict_hurricanes(name_lst, month_lst, year_lst, wind_lst, area_lst, damage_lst, death_lst):
  hurricanes = {}
  for i in range(0, len(name_lst)):
    hurricanes[name_lst[i]] = {"Name": name_lst[i], "Month": month_lst[i], "Year": year_lst[i],"Max Sustained Wind": wind_lst[i],"Areas Affected": area_lst[i],"Damage": damage_lst[i],"Deaths": death_lst[i]}

  return hurricanes

#Call hurricanes dictionary sorted by name
hurricanes_dict = dict_hurricanes(names, months, years, max_sustained_winds, areas_affected,updated_damage, deaths)
#print(hurricanes_dict)

# 4
# Organizing by Year
def dict_hurricanes_year(year):
  hurricane_by_year = []
  for name in hurricanes_dict:
    if hurricanes_dict[name]['Year'] == year:
      hurricane_by_year.append(hurricanes_dict[name])
  #print(hurricane_by_year)
  return hurricane_by_year
 
#print(dict_hurricanes_year(2018))
#print(dict_hurricanes_year(1932))
#print(dict_hurricanes_year(1955))

# 5
# Counting Damaged Areas
affected_areas = {}

def count_affected_areas(dictionary):
  for name in dictionary:
    for area in dictionary[name]['Areas Affected']:
      if area in affected_areas:
        affected_areas[area] += 1
      else:
        affected_areas[area] = 1
  return affected_areas

# create dictionary of areas to store the number of hurricanes involved in
hurricanes_area_count = count_affected_areas(hurricanes_dict)
#print(hurricanes_area_count)



# 6 
# Calculating Maximum Hurricane Count
def calculate_max_hurricane_count(affected_area_dict):
  max_count = 0
  for key, value in affected_area_dict.items():
    if value > max_count:
      max_count = value
      max_area = key
  return "{0} has the highest hurricane count with {1}".format(max_area, str(max_count))

# find most frequently affected area and the number of hurricanes involved in
print(calculate_max_hurricane_count(hurricanes_area_count))

# 7
# Calculating the Deadliest Hurricane
def greatest_number_of_deaths(dictionary):
  max_mortality = 0
  for name, value in dictionary.items():
    if max_mortality < value['Deaths']:
      max_mortality_cane = name
      max_mortality = value['Deaths']
  return "Hurricane {0} had the highest mortality rate with {1} deaths.".format(max_mortality_cane, str(max_mortality))

# find highest mortality hurricane and the number of deaths
print(greatest_number_of_deaths(hurricanes_dict))

# 8
# Rating Hurricanes by Mortality
def hurricane_categarised_by_mortality_rate(dictionary):
  mortality_scale = {0: 0,
                   1: 100,
                   2: 500,
                   3: 1000,
                   4: 10000}
  hurricanes_by_mortality = {0:[],1:[],2:[],3:[],4:[]}
  for name, value in dictionary.items():
    if value['Deaths'] > 0 and value['Deaths'] <= 100:
      hurricanes_by_mortality[0].append(value)
    elif value['Deaths'] > 100 and value['Deaths'] <= 500:
      hurricanes_by_mortality[1].append(value)
    elif value['Deaths'] > 500 and value['Deaths'] <=1000:
      hurricanes_by_mortality[2].append(value)
    elif value['Deaths'] > 1000 and value['Deaths'] <=10000:
      hurricanes_by_mortality[3].append(value)
    elif value['Deaths'] > 10000:
      hurricanes_by_mortality[4].append(value)  
  return hurricanes_by_mortality
  
# categorize hurricanes in new dictionary with mortality severity as key
#print(hurricane_categarised_by_mortality_rate(hurricanes_dict))

# 9 Calculating Hurricane Maximum Damage
def greatest_damage(dictionary):
  max_damage_cane = ''
  max_damage = 0
  for name, value in dictionary.items():
    if value['Damage'] == 'Damages not recorded':
      pass
    elif max_damage < int(value['Damage']):
      max_damage = value['Damage']
      max_damage_cane = name
  return "{0} Caused the most damage in monetary terms costing ${1} USD.".format(max_damage_cane, str(max_damage))

# find highest damage inducing hurricane and its total cost
print(greatest_damage(hurricanes_dict))

# 10
# Rating Hurricanes by Damage
def hurricane_categarised_by_damage_cost(dictionary):
  damage_scale = {0: 0,
                1: 100000000,
                2: 1000000000,
                3: 10000000000,
                4: 50000000000}
  hurricanes_by_damage = {0:[],1:[],2:[],3:[],4:[]}
  for name, value in dictionary.items():
    if value['Damage'] == 'Damages not recorded':
      pass
    elif value['Damage'] > 0 and value['Damage'] <= 100000000:
      hurricanes_by_damage[0].append(value)
    elif value['Damage'] > 100000000 and value['Damage'] <= 1000000000:
      hurricanes_by_damage[1].append(value)
    elif value['Damage'] > 1000000000 and value['Damage'] <=1000:
      hurricanes_by_damage[2].append(value)
    elif value['Damage'] > 10000000000 and value['Damage'] <=50000000000:
      hurricanes_by_damage[3].append(value)
    elif value['Damage'] > 50000000000:
      hurricanes_by_damage[4].append(value)  
  return hurricanes_by_damage

# categorize hurricanes in new dictionary with damage severity as key
#print(hurricane_categarised_by_damage_cost(hurricanes_dict))