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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]

# 1
# Update Recorded Damages
conversion = {"M": 1000000,
              "B": 1000000000}
def update_damage_units(damages):
  adj_damages = []
  for value in damages:
    if "B" in value:
      x = float(value[:-1])
      adj_damages.append(int(x*1000000000))
    elif "M" in value:
      x = float(value[:-1])
      adj_damages.append(int(x*1000000))
    else:
      adj_damages.append(value)
  return adj_damages

# test function by updating damages
adj_damages = update_damage_units(damages) 

# 2 
# Create a Table
def create_table(name,month,year,max_sustained_wind,areas_affected,death,damage):
  gen_table = {}
  for x in range(34):
    gen_table.update({name[x]:{"Name":name[x],"Month":month[x],"Year":year[x],"Max Sustained Wind":max_sustained_wind[x],"Areas Affected":areas_affected[x],"Deaths":death[x],"Damage":damage[x]}})
  return gen_table

# Create and view the hurricanes dictionary
hurricanes = create_table(names,months,years,max_sustained_winds,areas_affected,deaths,adj_damages)
#print(hurricanes)
# 3
# Organizing by Year
def sortByyear(sel_year):
  hurricane_by_year = {}
  for hurricane in hurricanes.values():
    if hurricane.get("Year") == sel_year:
      hurricane_by_year.update(hurricane)
  result = {sel_year:hurricane_by_year}
  return result
  
# create a new dictionary of hurricanes with year and key
# dictonary with year as key 
test_1932_hurricane_dict = sortByyear(1932)
#print(test_1932_hurricane_dict)

# 4
# Counting Damaged Areas
def hit_frequency(hurricanes):
  hit_freq = {}
  for hurricane in hurricanes.values():
    for location in hurricane.get("Areas Affected"):
      if location not in hit_freq:
        hit_freq[location] = 1
      else:
        hit_freq[location] += 1
  return hit_freq
#test dictonary function uncomment line below:
#print(hit_frequency(hurricanes))

# create dictionary of areas to store the number of hurricanes involved in
area_impact_count = hit_frequency(hurricanes)
# 5 
# Calculating Maximum Hurricane Count
def find_max_hurricane(hurricanes):
  count_area = hit_frequency(hurricanes)
  worst_area = max(count_area, key = count_area.get)
  return worst_area


# find most frequently affected area and the number of hurricanes involved in
most_affected_area =find_max_hurricane(hurricanes)

# 6
# Calculating the Deadliest Hurricane
def death_count(hurricanes):
  max_deaths = 0
  deadliest_hurricane = ""
  for hurricane, data in hurricanes.items():
    if data.get("Deaths") > max_deaths:
      max_deaths = data.get("Deaths")
      deadliest_hurricane = hurricane
  return deadliest_hurricane, max_deaths


# find highest mortality hurricane and the number of deaths
#print(death_count(hurricanes))
# 7
# Rating Hurricanes by Mortality
ortality_scale = {0: 0,
                   1: 100,
                   2: 500,
                   3: 1000,
                   4: 10000}
def rank_by_death_rating(hurricanes):
  dict_store0 = []
  dict_store1 = []
  dict_store2 = []
  dict_store3 = []
  dict_store4 = []
  dict_return = {}
  for name, data in hurricanes.items():
    if 0 <= data.get("Deaths") <100 :
      dict_store0.append(name) 
    elif 100 <= data.get("Deaths") <500:
      dict_store1.append(name)
    elif 500 <= data.get("Deaths") < 100:
      dict_store2.append(name)
    elif 1000 <= data.get("Deaths") <10000:
      dict_store3.append(name)
    else:
      dict_store4.append(name)
  rated_hurricane = {0:dict_store0,1:dict_store1,2:dict_store2,3:dict_store3,4:dict_store4}
  return rated_hurricane
# categorize hurricanes in new dictionary with mortality severity as key
hurricane_death_rating = rank_by_death_rating(hurricanes)
#print(hurricane_death_rating)

# 8 Calculating Hurricane Maximum Damage
def max_damage_cost(hurricanes):
  max_cost = 0
  max_cost_hurricane = ""
  for hurricane , data in hurricanes.items():
    if type(data.get("Damage")) != str:
      if data.get("Damage")>max_cost:
        max_cost = data.get("Damage")
        max_cost_hurricane = hurricane
  return max_cost_hurricane, max_cost

# find highest damage inducing hurricane and its total cost
most_expensive_hurricane = max_damage_cost(hurricanes)
print(most_expensive_hurricane)

# 9
# Rating Hurricanes by Damage
damage_scale = {0: 0,
                1: 100000000,
                2: 1000000000,
                3: 10000000000,
                4: 50000000000}
def rank_by_damage_rating(hurricanes):
  dict_store0 = []
  dict_store1 = []
  dict_store2 = []
  dict_store3 = []
  dict_store4 = []
  dict_return = {}
  for name, data in hurricanes.items():
    if type(data.get("Damage")) != str:
      if 0 <= data.get("Damage") <100000000 :
        dict_store0.append(name) 
      elif 100000000 <= data.get("Damage") <1000000000:
        dict_store1.append(name)
      elif 1000000000 <= data.get("Damage") < 10000000000:
        dict_store2.append(name)
      elif 10000000000 <= data.get("Damage") <50000000000:
        dict_store3.append(name)
      else:
        dict_store4.append(name)
  rated_hurricane = {0:dict_store0,1:dict_store1,2:dict_store2,3:dict_store3,4:dict_store4}
  return rated_hurricane
# categorize hurricanes in new dictionary with damage severity as key
print(rank_by_damage_rating(hurricanes))