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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 update_damages(cost):
  updated_damages = [] 
  for x in cost:
    if x == "Damages not recorded":
        updated_damages.append(x)
    elif x.endswith("B"):
        x = x.strip("B")
        x = float(x) * (10**9)
        updated_damages.append(x)
    elif x.endswith("M"):
        x = x.strip("M")
        x = float(x) * (10**6)
        updated_damages.append(x)
  return updated_damages
damages_float = update_damages(damages)
# test function by updating damages
#print(update_damages(damages))

# 2 
# Create and view the hurricanes dictionary
def hurricane_info():
  hurricanes_by_name = {}
  for x in names:
    index = names.index(x)
    hurricane_data = {"Name": names[index], "Month": months[index], "Year": years[index], "Max Sustained Winds": max_sustained_winds[index], "Areas Affected": areas_affected[index], "Damage": damages[index], "Deaths": deaths[index]}
    hurricanes_by_name[x] = hurricane_data
  return hurricanes_by_name
hurricanes = hurricane_info()
print(hurricanes)

# Create a Table (sort of)
for name, data in hurricanes.items():
  print(name + "    " + data["Month"] + "   " + str(data["Year"]) + "   " + str(data["Max Sustained Winds"]) + "   " + data["Damage"] + "   " + str(data["Deaths"]))
# 3
# create a new dictionary of hurricanes with year and key
hurricanes_by_year = {}  
for x in range(len(years)):
  new_hurricane_data = {}
  new_hurricane_data.update({years[x]: {"Name": names[x], "Month": months[x], "Year": years[x], "Max Sustained Winds": max_sustained_winds[x], "Areas Affected": areas_affected[x], "Damage": damages[x], "Deaths": deaths[x]}})
  hurricanes_by_year.update(new_hurricane_data)
print(hurricanes_by_year)
# Organizing by Year
for key in sorted(hurricanes_by_year.keys()):
  print('{}: {}'.format(key, hurricanes_by_year[key]))

# 4
# Counting Damaged Areas

# create dictionary of areas to store the number of hurricanes involved in
areas_only = []
for area in areas_affected:
    for x in area:
      areas_only.append(x)     
print(areas_only)
area_counts = []
for x in areas_only:
    area_counts.append(areas_only.count(x))
print(area_counts)
affected_counts = {}
merged_data = {key:value for key, value in zip(areas_only, area_counts)}
affected_counts.update(merged_data)
print(affected_counts)

# 5 
# Calculating Maximum Hurricane Count

# find most frequently affected area and the number of hurricanes involved in
area_freq = ""
area_freq = ([x for x in affected_counts.keys() if affected_counts[x] == max(affected_counts.values())][0])
print('{} is the most affected area. It has been affected by {} hurricanes.'.format(area_freq, affected_counts[area_freq]))

# 6
# Calculating the Deadliest Hurricane

# find highest mortality hurricane and the number of deaths
hurricane_deaths = {}
hurricane_deaths.update({key:value for key, value in zip(names, deaths)})     
most_deaths = ""
most_deaths = ([x for x in hurricane_deaths.keys() if hurricane_deaths[x] == max(hurricane_deaths.values())][0])
print("Hurricane {} is the deadliest hurricane with {} deaths.".format(most_deaths, hurricane_deaths[most_deaths]))
# 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}
death_ratings = {0: [], 1: [], 2: [], 3: [], 4: [], 5: []}
#print(deaths)
for name in hurricanes:
    x = hurricanes[name]["Deaths"]
    if x == 0:
      death_ratings[0].append(name)
    elif 0 < x <= 100:
      death_ratings[1].append(name)
    elif x <= 500 and x > 100:
      death_ratings[2].append(name)
    elif x <= 1000 and x > 500:
      death_ratings[3].append(name) 
    elif x <= 10000 and x > 1000:
      death_ratings[4].append(name)
    elif x > 10000:
      death_ratings[5].append(name)
print(death_ratings)
# 8 Calculating Hurricane Maximum Damage

# find highest damage inducing hurricane and its total cost
updates_damages = []
for float in damages_float:
    if float == "Damages not recorded":
        continue
    else:
        updates_damages.append(int(float))
#print(updates_damages)
updates_names = []
for name in hurricanes:
    if hurricanes[name]["Damage"] == "Damages not recorded":
        continue
    else:
        updates_names.append(name)
#print(updates_names)
damaging_canes = {}
merged_data2 = {key:value for key, value in zip(updates_names, updates_damages)}
damaging_canes.update(merged_data2)
#print(damaging_canes)
most_damage = ""
most_damage = ([x for x in damaging_canes.keys() if damaging_canes[x] == max(damaging_canes.values())][0])
print('{} was the costliest hurricane incurring {} dollars in damages.'.format(most_damage, damaging_canes[most_damage]))
#for key in sorted(damaging_canes.keys()):
    #print('{}: {}'.format(key, damaging_canes[key]))
this = list(damaging_canes.items())
l = list(this)
m = sorted(l, key=lambda x: x[1])
print(m)
# 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
damages2 = []
for float in damages_float:
    if float == "Damages not recorded":
        damages2.append(float)
    else:
        damages2.append(int(float))
#print(damages2)
def dam_canes():
  hurricanes_by_damage = {}  
  for item in names:
    index = names.index(item)
    damage_data = {"Name": names[index], "Damage": damages2[index]}
    hurricanes_by_damage[item] = damage_data
  return hurricanes_by_damage
canes_dam = dam_canes()
#print(canes_dam)

damage_ratings = {0: [], 1: [], 2: [], 3: [], 4: [], 5: [], None: []}
for name in canes_dam:
    x = canes_dam[name]["Damage"]
    if x == "Damages not recorded":
      damage_ratings[None].append(name)
      continue
    elif x == 0:
      damage_ratings[0].append(name)
    elif 0 < x <= 100000000:
      damage_ratings[1].append(name)
    elif x <= 1000000000 and x > 100000000:
      damage_ratings[2].append(name)
    elif x <= 10000000000 and x > 1000000000:
      damage_ratings[3].append(name) 
    elif x <= 50000000000 and x > 10000000000:
      damage_ratings[4].append(name)
    elif x > 50000000000:
      damage_ratings[5].append(name)
print(damage_ratings)