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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 convert_damages(damages):
  new_damages = []
  for damage in damages:
    if "M" in damage:
      new_damages.append(float(damage.strip("M")) * 1000000)
    elif "B" in damage:
      new_damages.append(float(damage.strip("B")) * 1000000000)
    else:
      new_damages.append(damage)
  return new_damages
# test function by updating damages
#print(convert_damages(damages))
#print(damages)

# 2 
# Create a Table

# Create and view the hurricanes dictionary
def create_hurricane_dict(names, months, years, wind_max, areas, damage, death):
  new_dict = {}
  for i in range(len(names)):
    new_dict[names[i]] = {"Name": names[i], "Month": months[i], "Year": years[i], "Max Sustained Wind": wind_max[i], "Areas Affected": areas[i], "Damage": damage[i], "Deaths": death[i]}
  return new_dict

new_hurricane_dictionary = create_hurricane_dict(names, months, years, max_sustained_winds, areas_affected, damages, deaths)
#print(new_hurricane_dictionary)
# 3
# Organizing by Year
def convert_to_year(hurricane_dict):
  new_dict = {}
  for key, value in hurricane_dict.items():
    current_year = value["Year"]
    current_cane = value
    if current_year in new_dict:
      new_dict[current_year].append(value)
    else:
      new_dict[current_year] = []
      new_dict[current_year].append(value)
  return new_dict
# create a new dictionary of hurricanes with year and key
new_dated_hurricane_dictionary = convert_to_year(new_hurricane_dictionary)
#print(new_dated_hurricane_dictionary)


# 4
# Counting Damaged Areas
def count_damaged_areas(hurricane_dict):
  new_dict = {}
  for key, value in hurricane_dict.items():
    for area in value["Areas Affected"]:
      if area not in new_dict:
        new_dict[area] = 1
      else:
        new_dict[area] += 1
  return new_dict

# create dictionary of areas to store the number of hurricanes involved in


# 5 
# Calculating Maximum Hurricane Count
#new_hurricane_dictionary.update({'Greg': {'Name': 'Greg', 'Month': 'July', 'Year': 2021, 'Max Sustained Wind': 200, 'Areas Affected': ['The Caribbean'], 'Damage': '64.8B', 'Deaths': 8}})
#print(count_damaged_areas(new_hurricane_dictionary))
# find most frequently affected area and the number of hurricanes involved in
def most_affected_area(hurricane_dict):
  new_dict = count_damaged_areas(hurricane_dict)
  most_affected = {}
  most_affected_times = 0
  for key, value in new_dict.items():
    if value > most_affected_times:
      most_affected_times = value
      most_affected.clear()
      most_affected.update({key:value})
    elif value == most_affected_times:
      most_affected.update({key:value})
  for key, value in most_affected.items():
    print("{key} was hit {value} times.".format(key=key, value=value))
#most_affected_area(new_hurricane_dictionary)
# 6
# Calculating the Deadliest Hurricane

# find highest mortality hurricane and the number of deaths
def most_deaths(hurricane_dict):
  highest_death_hurricanes = {}
  highest_death_count = 0
  for key, value in hurricane_dict.items():
    current_deaths = value["Deaths"]
    if current_deaths > highest_death_count:
      highest_death_count = current_deaths
      highest_death_hurricanes.clear()
      highest_death_hurricanes.update({key:current_deaths})
    elif current_deaths == highest_death_count:
      highest_death_hurricanes.update({key:current_deaths})
  for key, value in highest_death_hurricanes.items():
    print("{key} caused {value} deaths.".format(key=key, value=value))

#most_deaths(new_hurricane_dictionary)

# 7
# Rating Hurricanes by Mortality


# categorize hurricanes in new dictionary with mortality severity as key
def most_mortal(hurricane_dict):
  new_dict = {0:[], 1:[], 2:[], 3:[], 4:[], 5:[]}
  for value in hurricane_dict.values():
    if value["Deaths"] == 0:
      new_dict[0].append(value)
    elif value["Deaths"] <= 100:
      new_dict[1].append(value)
    elif value["Deaths"] <= 500:
      new_dict[2].append(value)
    elif value["Deaths"] <= 1000:
      new_dict[3].append(value)
    elif value["Deaths"] <= 10000:
      new_dict[4].append(value)
    elif value["Deaths"] > 10000:
      new_dict[5].append(value)
  return new_dict

mortality_scale_dictionary = most_mortal(new_hurricane_dictionary)
#print(mortality_scale_dictionary)

    


# 8 Calculating Hurricane Maximum Damage

# find highest damage inducing hurricane and its total cost
def convert_damage(damage):
    if "M" in damage:
      return float(damage.strip("M")) * 1000000
    elif "B" in damage:
      return float(damage.strip("B")) * 1000000000
    else:
      return 0

def maximum_damage(hurricane_dict):
  new_dict = {}
  max_damage = 0
  for key, value in hurricane_dict.items():
    current_damage = convert_damage(value["Damage"])
    if current_damage == max_damage:
      new_dict.update({key:current_damage})
    elif current_damage > max_damage:
      new_dict.clear()
      new_dict.update({key:current_damage})
  return new_dict

#print(maximum_damage(new_hurricane_dictionary))


# 9
# Rating Hurricanes by Damage
damage_scale = {0: 0,
                1: 100000000,
                2: 1000000000,
                3: 10000000000,
                4: 50000000000}
  
# categorize hurricanes in new dictionary with damage severity as key

def most_damaging(hurricane_dict):
  new_dict = {0:[], 1:[], 2:[], 3:[], 4:[], 5:[]}
  for value in hurricane_dict.values():
    current_damage = convert_damage(value["Damage"])
    if current_damage == 0:
      new_dict[0].append(value)
    elif current_damage <= 100000000:
      new_dict[1].append(value)
    elif current_damage <= 1000000000:
      new_dict[2].append(value)
    elif current_damage <= 10000000000:
      new_dict[3].append(value)
    elif current_damage <= 50000000000:
      new_dict[4].append(value)
    elif current_damage > 50000000000:
      new_dict[5].append(value)
  return new_dict

#print(most_damaging(new_hurricane_dictionary))