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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}

# test function by updating damages
def convert_to_num(damages):
  damages_converted = []
  for damage in damages:
    if(damage == 'Damages not recorded'):
      damages_converted.append(damage)
    else:
      decimal_places = conversion[damage[-1]]
      damage_num = float(damage[:-1]) * decimal_places
      damages_converted.append(damage_num)
  return damages_converted
# print(convert_to_num(damages))

# 2 
# Create a Table
def create_hurricane_table(names, months, years, max_sustained_winds, areas_affected, damages, deaths):
  hurricane_table = {}
  zipped_datas = zip(names, months, years, max_sustained_winds, damages, deaths)
  area_affected = 0
  for name, month, year, max_sustained_wind, damage, deaths in zipped_datas:
    hurricane_table[name] = {'Name': name, 'Month': month, 'Year': year, 'Max Sustained Wind': max_sustained_wind, 'Areas Affected': areas_affected[area_affected], 'Damage': damage, 'Deaths': deaths}
    area_affected += 1
  return hurricane_table
hurricane_table = create_hurricane_table(names, months, years, max_sustained_winds, areas_affected, damages, deaths)
# Create and view the hurricanes dictionary
# print(hurricane_table)

# 3
# Organizing by Year
def hurricane_by_year(hurricane_table, years):
  hurricanes_by_year = {}

  for year in years:
    hurricanes_by_year[year] = list()

  for year in years:
    for hurricane_datas in hurricane_table.values():
      if hurricane_datas['Year'] == year:
        hurricanes_by_year[year].append(hurricane_datas)
  return hurricanes_by_year
# create a new dictionary of hurricanes with year and key
hurricanes_by_years = hurricane_by_year(hurricane_table, years)
# print(hurricanes_by_years)

# 4
# Counting Damaged Areas
def frequency_affected_area(hurricane_table, areas_affected):
  affected_area_count = {}
  for area_affected in areas_affected:
    for area in area_affected:
      affected_area_count[area] = 0
  
  for hurricane_data in hurricane_table.values():
    affected_areas = hurricane_data['Areas Affected']

    for area in affected_areas:
      affected_area_count[area] += 1
  
  return affected_area_count
# create dictionary of areas to store the number of hurricanes involved in
affected_area_count = frequency_affected_area(hurricane_table, areas_affected)
# print(affected_area_count)

# 5 
# Calculating Maximum Hurricane Count
def most_affected_area(affected_area_count):
  pair_area = ['', 0]
  for area, frequency in affected_area_count.items():
    if pair_area[1] <= frequency:
      pair_area = [area, frequency]
  return pair_area
# find most frequently affected area and the number of hurricanes involved in
most_affected = most_affected_area(affected_area_count)
# print(most_affected)

# 6
# Calculating the Deadliest Hurricane
def most_lethal_hurricane(hurricane_table):
  pair_hurricane = ['', 0]
  for hurricane_data in hurricane_table.values():
    if pair_hurricane[1] <= hurricane_data['Deaths']:
      pair_hurricane[0] = hurricane_data['Name']
      pair_hurricane[1] = hurricane_data['Deaths']
  return pair_hurricane

# find highest mortality hurricane and the number of deaths
most_lethal = most_lethal_hurricane(hurricane_table)
# print(most_lethal)

# 7
# Rating Hurricanes by Mortality
mortality_scale = {0: 0,
                   1: 100,
                   2: 500,
                   3: 1000,
                   4: 10000}
def hurricane_by_mortality(hurricane_table, mortality_scale):
  hurricane_by_mortality = {}

  for mortality in range(0, 6):
    hurricane_by_mortality[mortality] = list()

  for hurricane_data in hurricane_table.values():
    if hurricane_data['Deaths'] > 10000:
      hurricane_by_mortality[5].append(hurricane_data)
    else:
      for mortality in mortality_scale.keys():
        if hurricane_data['Deaths'] <= mortality_scale[mortality]:
          hurricane_by_mortality[mortality].append(hurricane_data)
          break
  return hurricane_by_mortality

# categorize hurricanes in new dictionary with mortality severity as key
hurricane_by_mortality = hurricane_by_mortality(hurricane_table, mortality_scale)
# print(hurricane_by_mortality)

# 8 Calculating Hurricane Maximum Damage
def damage_as_num(damage):
  if(damage == 'Damages not recorded'):
    return -1
  else:
    decimal_places = conversion[damage[-1]]
    damage_num = float(damage[:-1]) * decimal_places
    return damage_num

def most_damage_hurricane(hurricane_table):
  pair_hurricane = ['', 0]
  for hurricane_data in hurricane_table.values():
    damage = damage_as_num(hurricane_data['Damage'])
    if pair_hurricane[1] <= damage:
      pair_hurricane[0] = hurricane_data['Name']
      pair_hurricane[1] = damage
  return pair_hurricane

# find highest damage inducing hurricane and its total cost
damage_hurricane = most_damage_hurricane(hurricane_table)
# print(damage_hurricane)

# 9
# Rating Hurricanes by Damage
damage_scale = {0: 0,
                1: 100000000,
                2: 1000000000,
                3: 10000000000,
                4: 50000000000}
  
def hurricane_by_damage(hurricane_table, damage_scale):
  hurricane_by_damage = {}

  for damage in range(0, 6):
    hurricane_by_damage[damage] = list()

  for hurricane_data in hurricane_table.values():
    damage_hurricane = damage_as_num(hurricane_data['Damage'])
    if damage_hurricane > 50000000000:
      hurricane_by_mortality[5].append(hurricane_data)
    else:
      for damage in damage_scale.keys():
        if damage_hurricane <= damage_scale[damage]:
          hurricane_by_damage[damage].append(hurricane_data)
          break
  return hurricane_by_damage

# categorize hurricanes in new dictionary with damage severity as key
hurricanes_by_damage = hurricane_by_damage(hurricane_table, damage_scale)
# print(hurricanes_by_damage)