Codecademy export

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]

# write your update damages function here:

def update_damages(list):
  new_list = []
  for i in damages:
    if i[-1] == 'B':
      i = float(i[:-1])*1000000000
      new_list.append(i)
    elif i[-1] == 'M':
      i = float(i[:-1])*1000000
      new_list.append(i)
    else:
      new_list.append(i)
  return new_list

updated_damages = update_damages(damages)
#print(updated_damages)


# write your construct hurricane dictionary function here:
hurricanes = {}
def build_hurricane_dict(names, months, years, max_sustained_winds, areas_affected, updated_damages, deaths):
  for i in range(len(names)):
    hurricanes.update({names[i]: {'Name': names[i], 'Month': months[i], 'Year': years[i],'Max Sustained Wind': max_sustained_winds[i], 'Areas Affected': areas_affected[i],'Damage': updated_damages[i], 'Death': deaths[i]}})

build_hurricane_dict(names, months, years, max_sustained_winds, areas_affected, updated_damages, deaths)
#print(hurricanes)

# write your construct hurricane by year dictionary function here:
hurricanes_by_year = {}
data = list(hurricanes.values())
def group_by_year(): 
  for i in range(len(years)):
    key = years[i]
    value = data[i]
    if key not in hurricanes_by_year:
      hurricanes_by_year[key]=[value]
    else:
      hurricanes_by_year[key].append(value)

group_by_year()    
#print(hurricanes_by_year)

# write your count affected areas function here:
areas_affected_frequency = {}
def areas_affected_count():
  for i in areas_affected:
    for location in i:
      if location not in areas_affected_frequency:
        areas_affected_frequency[location] = 1
      else:
        areas_affected_frequency[location] += 1

areas_affected_count()
#print(areas_affected_frequency)
  

# write your find most affected area function here:
def most_affected_area():
  max_value = max(areas_affected_frequency.values())
  for i in areas_affected_frequency:
    if areas_affected_frequency[i] == max_value:
      print(i + ' was the most affected area, hit ' + str(max_value) + ' times.')
most_affected_area()

# write your greatest number of deaths function here:
def highest_death_toll():
  deaths_by_hurricane = list(zip(deaths, names))
  print(max(deaths_by_hurricane))
highest_death_toll()

# write your catgeorize by mortality function here:
hurricane_mortality_rating = {1:[], 2:[], 3:[], 4:[], 5:[]}
def mortality_rating():
  for i in data:
    if int(i["Death"]) <= 100:
      hurricane_mortality_rating[1].append(i['Name'])
    elif int(i["Death"]) <= 500:
      hurricane_mortality_rating[2].append(i['Name'])
    elif int(i["Death"]) <= 1000:
      hurricane_mortality_rating[3].append(i['Name'])
    elif int(i["Death"]) <= 10000:
      hurricane_mortality_rating[4].append(i['Name'])
    else:
      hurricane_mortality_rating[5].append(i['Name'])
  print(hurricane_mortality_rating)
mortality_rating()

# write your greatest damage function here:
def greatest_damage():
  int_damages = [i for i in updated_damages if i != 'Damages not recorded']
  for i in data:
    if (i['Damage']) == max(int_damages):
      print(i['Name'] + ' caused the most damage, costing $' + str(i['Damage']))
greatest_damage()

# write your catgeorize by damage function here:
hurricane_cost_scale = {0:[], 1:[], 2:[], 3:[], 4:[], 5:[]}
def cost_rating():
  for i in data:
    if i['Damage'] == "Damages not recorded":
      hurricane_cost_scale[0].append(i['Name'])
    elif i['Damage'] <= 100000000:
      hurricane_cost_scale[1].append(i['Name'])
    elif i['Damage'] <= 1000000000:
      hurricane_cost_scale[2].append(i['Name'])
    elif i['Damage'] <= 10000000000:
      hurricane_cost_scale[3].append(i['Name'])
    elif i['Damage'] <= 500000000000:
      hurricane_cost_scale[4].append(i['Name'])
    else:
      hurricane_cost_scale[5].append(i['Name'])

cost_rating()
print(hurricane_cost_scale)