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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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