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]

# 1
# Update Recorded Damages
conversion = {"M": 1000000,
              "B": 1000000000}
def update_damages(damages):
  damages_updated = []
  for item in damages:
    if item[-1] == "M":
      damages_updated.append(float(item[:-1]) * 1000000)
    elif item[-1] == "B":
      damages_updated.append(float(item[:-1]) * 1000000000)
    else:
      damages_updated.append("Damages not recorded")
  return damages_updated
damages_updated = update_damages(damages)
# test function by updating damage

# 2 
# Create a Table
# Create and view the hurricanes dictionary
def hurricanes_dict(names, months, years, max_sustained_winds, areas_affected, damages, deaths):
  hurricanes = {}
  counter = 0
  for item in names:
    hurricanes.update({names[counter]: {"Name": names[counter], "Month": months[counter], "Year": years[counter], "Max Sustained Wind": max_sustained_winds[counter], "Areas Affected": areas_affected[counter], "Damage": damages[counter], "Deaths": deaths[counter]}})
    counter += 1
  return hurricanes
hurricanes = hurricanes_dict(names, months, years, max_sustained_winds, areas_affected, damages_updated, deaths)

# 3
# Organizing by Year
# create a new dictionary of hurricanes with year and key
def convert_by_year(hurricanes):
  dictionary = {}
  for value in hurricanes:
    current_year = hurricanes[value]["Year"]
    current_value = hurricanes[value]
    if current_year not in dictionary:
      dictionary[current_year] = [current_value]
    else:
      dictionary[current_year].append(current_value)
  return dictionary
 
# 4
# Counting Damaged Areas
# create dictionary of areas to store the number of hurricanes involved in
def hurricanes_frequency(hurricanes):
  new_dict = {}
  for value in hurricanes:
    for area in hurricanes[value]["Areas Affected"]:
      if area not in new_dict:
        new_dict[area] = 1
      else:
        new_dict[area] += 1
  return new_dict 
 
# 5 
# Calculating Maximum Hurricane Count
# find most frequently affected area and the number of hurricanes involved in
affected_areas = hurricanes_frequency(hurricanes)
def maximum_count(affected_areas):
  max_area = "Central America"
  max_area_count = 0
  for key in affected_areas:
    if max_area_count < affected_areas[key]:
      max_area = key
      max_area_count = affected_areas[key]
  print("The most affected area is " + max_area + ". There were " + str(max_area_count) + " hurricanes registered there.")

# 6
# Calculating the Deadliest Hurricane
def maximum_deaths(hurricanes):
  deadliest_hurricane = ""
  max_deaths = 0
  for key in hurricanes:
    if max_deaths < hurricanes[key]["Deaths"]:
      max_deaths = hurricanes[key]["Deaths"]
      deadliest_hurricane = hurricanes[key]["Name"]
  print("The deadliest hurricane was " + deadliest_hurricane + ". It caused " + str(max_deaths) + " deaths.")

maximum_deaths(hurricanes)

# 7
# Rating Hurricanes by Mortality
# categorize hurricanes in new dictionary with mortality severity as key
mortality_scale = {0: 0,
                   1: 100,
                   2: 500,
                   3: 1000,
                   4: 10000}
def hurricanes_by_mortality(hurricanes):
  new_dict = {0:[], 1:[], 2:[], 3:[], 4:[], 5:[]}
  for key in hurricanes:
    if hurricanes[key]["Deaths"] > mortality_scale[4]:
      new_dict[5].append(hurricanes[key])
    elif hurricanes[key]["Deaths"] > mortality_scale[3]:
      new_dict[4].append(hurricanes[key])
    elif hurricanes[key]["Deaths"] > mortality_scale[2]:
      new_dict[3].append(hurricanes[key])
    elif hurricanes[key]["Deaths"] > mortality_scale[1]:
      new_dict[2].append(hurricanes[key])
    elif hurricanes[key]["Deaths"] > mortality_scale[0]:
      new_dict[1].append(hurricanes[key])
    else:
      new_dict[0].append(hurricanes[key])
  return new_dict
# 8 Calculating Hurricane Maximum Damage
# find highest damage inducing hurricane and its total cost
def maximum_damage(hurricanes):
  most_damaging_hurricane = ""
  max_cost = 0
  for key in hurricanes:
    if type(hurricanes[key]["Damage"]) is float and max_cost < hurricanes[key]["Damage"]:
      max_cost = hurricanes[key]["Damage"]
      most_damaging_hurricane = hurricanes[key]["Name"]
  print("The hurricane that caused the greatest damage was " + most_damaging_hurricane + ". It cost " + str(max_cost) + " dollars.")

maximum_damage(hurricanes)

# 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 hurricanes_by_damage_f(hurricanes):
  hurricanes_by_damage = {0:[], 1:[], 2:[], 3:[], 4:[], 5:[]}
  for key in hurricanes:
    if type(hurricanes[key]["Damage"]) is float and hurricanes[key]["Damage"] > damage_scale[4]:
      hurricanes_by_damage[5].append(hurricanes[key])
    elif type(hurricanes[key]["Damage"]) is float and hurricanes[key]["Damage"] > damage_scale[3]:
      hurricanes_by_damage[4].append(hurricanes[key])
    elif type(hurricanes[key]["Damage"]) is float and hurricanes[key]["Damage"] > damage_scale[2]:
      hurricanes_by_damage[3].append(hurricanes[key])
    elif type(hurricanes[key]["Damage"]) is float and hurricanes[key]["Damage"] > damage_scale[1]:
      hurricanes_by_damage[2].append(hurricanes[key])
    elif type(hurricanes[key]["Damage"]) is float and hurricanes[key]["Damage"] > mortality_scale[0]:
      hurricanes_by_damage[1].append(hurricanes[key])
    else:
      hurricanes_by_damage[0].append(hurricanes[key])
  return hurricanes_by_damage