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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
}
updated_damages = []
def update_damages():
  for i in damages:
    if i == "Damages not recorded":
      updated_damages.append(i)
    else:
      if "M" in i:
        updated_damages.append(float(i[:-1]) * conversion["M"])
      else: 
        updated_damages.append(float(i[:-1]) * conversion["B"])

update_damages()
print("UPDATED DAMAGES: " + str(updated_damages))
print("------------------------------------------------------------")

# test function by updating damages


# 2 
# Create a Table
hurricanes = {}
def hurricanes_const():
  for i in range(len(list(zip(names, months, years, max_sustained_winds, areas_affected, updated_damages, deaths)))):
    hurricanes.update({names[i]: {"Name": names[i], "Month": months[i], "Year": years[i], "Max Sustained Winds": max_sustained_winds[i], "Areas Affected": areas_affected[i], "Damage": updated_damages[i], "Death": deaths[i]}})

hurricanes_const()
print("HURRICANES DICT: " + str(hurricanes))
print("------------------------------------------------------------")
# Create and view the hurricanes dictionary

# 3
# Organizing by Year

# create a new dictionary of hurricanes with year and key
canes_by_year = {}
def hurricanes_by_year():
  for hurricane, data in hurricanes.items():
    current_year = data['Year']
    canes_by_year.update({current_year: data})
hurricanes_by_year()
print("HURRICANES BY YEAR: " + str(canes_by_year))
print("-----------------------------------------------------")
# 4
# Counting Damaged Areas

# create dictionary of areas to store the number of hurricanes involved in
def count_areas_affected(dictionary):
  dct_area_counts = {}
  for hurricane in hurricanes:
    current_cane = hurricanes[hurricane]
    lst_of_areas = current_cane["Areas Affected"]
    for area in lst_of_areas:
      if area in dct_area_counts.keys():
        dct_area_counts[area] += 1
      else:
        dct_area_counts[area] = 1
  return dct_area_counts

areas_affected = count_areas_affected(hurricanes)
print("AREAS AFFECTED: "+ str(areas_affected))
print("-----------------------------------------------------")
# 5 
# Calculating Maximum Hurricane Count

# find most frequently affected area and the number of hurricanes involved in
def most_affected(lst):
  max_area = "Central America"
  max_area_count = 0
  for current_area, current_count in lst.items():
    if max_area_count < current_count:
      max_area = current_area
      max_area_count = current_count
  return max_area, max_area_count
print("MOST AFFECTED: " + str(most_affected(areas_affected)).strip("()"))
print("-----------------------------------------------------")
# 6
# Calculating the Deadliest Hurricane

# find highest mortality hurricane and the number of deaths
def deadliest_hurricane():
  deadliest_area = ""
  num_deaths = 0
  for hurricane, data in hurricanes.items():
    current_cane = hurricane
    current_deaths = data['Death']
    if current_deaths > num_deaths:
      num_deaths = current_deaths
      deadliest_area = current_cane
  return deadliest_area, num_deaths

print("DEADLIEST HURRICANE: " + str(deadliest_hurricane()))
print("-----------------------------------------------------")
# 7
# Rating Hurricanes by Mortality

# categorize hurricanes in new dictionary with mortality severity as key
mort_ratings = {
  0: [],
  1: [], 
  2: [], 
  3: [], 
  4: [],
  5: []
}
def mortality():
  for hurricane, data in hurricanes.items():
    current_cane = hurricane
    num_deaths = data['Death']
    if num_deaths == 0:
      mort_ratings[0].append(current_cane)
    elif num_deaths > 0 and num_deaths <= 100:
      mort_ratings[1].append(current_cane)
    elif num_deaths > 101 and num_deaths <= 500:
      mort_ratings[2].append(current_cane)
    elif num_deaths > 501 and num_deaths <= 1000:
      mort_ratings[3].append(current_cane)
    elif num_deaths > 1000 and num_deaths <= 10000:
      mort_ratings[4].append(current_cane)
    else:
      mort_ratings[5].append(current_cane)
mortality()
print("MORTALITY RATINGS: " + str(mort_ratings))
print("-----------------------------------------------------")
# 8 Calculating Hurricane Maximum Damage

# find highest damage inducing hurricane and its total cost
def highest_cost_cane():
  max_damage_cane = ""
  max_damage = 0
  for hurricane, data in hurricanes.items():
    current_cane = hurricane
    current_damage = data['Damage']
    if "Damages not recorded" in hurricanes.values():
      return "Nothing reported"
    else:
      max_damage_cane = current_cane
      max_damage = current_damage
  return max_damage_cane, max_damage


print("MOST EXPENSIVE HURRICANE: " + str(highest_cost_cane()).strip('()'))
print("-----------------------------------------------------")
# 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
hurricane_dam_scale = {
  0: [],
  1: [],
  2: [],
  3: [],
  4: [],
  5: []
}
def canes_by_dam_rating():
  for hurricane, data in hurricanes.items():
    current_cane = hurricane
    current_damage = data['Damage']
    if current_damage == "Damages not recorded":
      continue
    elif current_damage > 0 and current_damage <= 100000000:
      hurricane_dam_scale[1].append(current_cane)
    elif current_damage > 100000001 and current_damage <= 1000000000:
      hurricane_dam_scale[2].append(current_cane)
    elif current_damage > 1000000001 and current_damage <= 10000000000:
      hurricane_dam_scale[3].append(current_cane)
    elif current_damage > 10000000001 and current_damage <= 50000000000:
      hurricane_dam_scale[4].append(current_cane)
    else:
      hurricane_dam_scale[5].append(current_cane)


canes_by_dam_rating()
print("DAMAGE RATING: " + str(hurricane_dam_scale))