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# 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)']]
# Fixing the affected areas data
upd_areas_list = [[area.upper() for area in areas] for areas in areas_affected]
#print(upd_areas_list)
# 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 upd_dmg(list):
damages_fix = []
for item in list:
if "M" in item:
j = item.replace("M", "000000")
k = j.replace(".", "")
damages_fix.append(float(k))
elif "B" in item:
j = item.replace("B", "000000000")
k = j.replace(".", "")
damages_fix.append(float(k))
else:
damages_fix.append(item)
return damages_fix
updated_dmg = upd_dmg(damages)
# 2
# Create a Table
# Create and view the hurricanes dictionary
def hurricane_dict(names, months, years, max_winds, areas, damage, deaths):
new_dict = {}
for item in range(len(names)):
new_dict[names[item]] = {
"Name": names[item],
"Month": months[item],
"Year": years[item],
"Max Sustained Wind": max_sustained_winds[item],
"Areas Affected": upd_areas_list[item],
"Damage": updated_dmg[item],
"Deaths": deaths[item]
}
return new_dict
hurricanes = hurricane_dict(names, months, years, max_sustained_winds, upd_areas_list, updated_dmg, deaths)
#print(hurricanes)
# 3
# Organizing by Year
def hurricanes_by_year(dic):
new_dic = {}
for key in dic:
current_year = dic[key]['Year']
current_cane = dic[key]
if current_year in new_dic:
new_dic[current_year].append(current_cane)
else:
new_dic[current_year] = [current_cane]
return new_dic
# create a new dictionaray of hurricanes with year and key
by_year = hurricanes_by_year(hurricanes)
#print(by_year)
# 4
# Counting Damaged Areas
def areas_count(hurricanes):
areas_count = {}
for hurricane in hurricanes:
for area in hurricanes[hurricane]["Areas Affected"]:
if area not in areas_count:
areas_count[area] = 1
else:
areas_count[area] += 1
return areas_count
# create dictionary of areas to store the number of hurricanes involved in
affected_areas_count = areas_count(hurricanes)
#print(affected_areas_count)
# 5
# Calculating Maximum Hurricane Count
def max_hurricane_count(lst):
max_area = ''
max_area_count = 0
for key, value in lst.items():
if value > max_area_count:
max_area_count = value
max_area = key
return "The most affected area is {}, which was hit {} times".format(max_area, max_area_count)
# find most frequently affected area and the number of hurricanes involved in
max_hurricane_count = max_hurricane_count(affected_areas_count)
#print(max_hurricane_count)
# 6
# Calculating the Deadliest Hurricane
def max_mortality_count(lst):
max_mort_cane = ''
max_mort = 0
year = 0
for hurricane in lst.values():
if hurricane["Deaths"] > max_mort:
max_mort = hurricane["Deaths"]
max_mort_cane = hurricane["Name"]
year = hurricane["Year"]
return "The deadlies Hurricanes is {}, which killed {} people in {}".format(max_mort_cane, max_mort, year)
# find highest mortality hurricane and the number of deaths
max_mortality_count = max_mortality_count(hurricanes)
#print(max_mortality_count)
# 7
# Rating Hurricanes by Mortality
def hurricanes_by_mortality(lst):
mortality = {0:[], 1:[], 2:[], 3:[], 4:[], 5:[]}
for hur in lst:
if lst[hur]["Deaths"] > 10000:
mortality[5].append(hur)
elif lst[hur]["Deaths"] > 1000:
mortality[4].append(hur)
elif lst[hur]["Deaths"] > 500:
mortality[3].append(hur)
elif lst[hur]["Deaths"] > 100:
mortality[2].append(hur)
elif lst[hur]["Deaths"] > 0:
mortality[1].append(hur)
else:
mortality[0].append(hur)
return mortality
# categorize hurricanes in new dictionary with mortality severity as key
mortality_scale = hurricanes_by_mortality(hurricanes)
#print(mortality_scale)
# 8 Calculating Hurricane Maximum Damage
def max_damage_count(lst):
max_dmg_cane = ''
max_dmg = 0
year = 0
for hurricane in lst.values():
if hurricane["Damage"] == "Damages not recorded":
pass
elif hurricane["Damage"] > max_dmg:
max_dmg = hurricane["Damage"]
max_dmg_cane = hurricane["Name"]
year = hurricane["Year"]
return "The most expensive Hurricane is {}, which cost {} dollars in {}".format(max_dmg_cane, max_dmg, year)
# find highest damage inducing hurricane and its total cost
max_dmg_hurr = max_damage_count(hurricanes)
#print(max_dmg_hurr)
# 9
# Rating Hurricanes by Damage
damage_scale = {0: 0,
1: 100000000,
2: 1000000000,
3: 10000000000,
4: 50000000000}
def calculate_dmg_scale(lst):
damage = {0:[], 1:[], 2:[], 3:[], 4:[], 5:[]}
for hur in lst:
if type(lst[hur]["Damage"]) is float or type(lst[hur]["Damage"]) is int:
if lst[hur]["Damage"] > 50000000000:
damage[5].append(hur)
elif lst[hur]["Damage"] > 10000000000:
damage[4].append(hur)
elif lst[hur]["Damage"] > 1000000000:
damage[3].append(hur)
elif lst[hur]["Damage"] > 100000000:
damage[2].append(hur)
elif lst[hur]["Damage"] > 0:
damage[1].append(hur)
else:
damage[0].append(hur)
return damage
# categorize hurricanes in new dictionary with damage severity as key
dmg_scale = calculate_dmg_scale(hurricanes)
#print(dmg_scale)
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