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}
# test function by updating damages
damages = [float(d[:-1]) * conversion[d[-1]] if d != 'Damages not recorded' else d for d in damages]
# 2
# Create a Table
# Create and view the hurricanes dictionary
hurricanes_dict = {names[i]: {'Name': names[i], 'Month': months[i], 'Year': years[i], 'Max Sustained Wind': max_sustained_winds[i], 'Areas Affected': areas_affected[i], 'Damage': damages[i], 'Deaths': deaths[i]} for i in range(len(names))}
# 3
# Organizing by Year
# create a new dictionary of hurricanes with year and key
by_year_dict = {year: [hurricanes_dict[hurricane] for hurricane in hurricanes_dict if hurricanes_dict[hurricane]['Year'] == year] for year in years}
# 4
# Counting Damaged Areas
# create dictionary of areas to store the number of hurricanes involved in
flat_list = [area for areas in areas_affected for area in areas]
area_counts = {area: flat_list.count(area) for area in set(flat_list)}
# 5
# Calculating Maximum Hurricane Count
# find most frequently affected area and the number of hurricanes involved in
most_freq = [item for item in area_counts.items() if item[1] == max(area_counts.values())]
# 6
# Calculating the Deadliest Hurricane
# find highest mortality hurricane and the number of deaths
deadliest = names[deaths.index(max(deaths))]
# 7
# Rating Hurricanes by Mortality
mortality_scale = {0: 0,
1: 100,
2: 500,
3: 1000,
4: 10000,
5: 20000}
# categorize hurricanes in new dictionary with mortality severity as key
mortality_dict = {grade: [hurricanes_dict[hurricane] for hurricane in hurricanes_dict if hurricanes_dict[hurricane]['Deaths'] <= mortality_scale[grade] and (hurricanes_dict[hurricane]['Deaths'] > mortality_scale[grade - 1] or grade == 0)] for grade in mortality_scale.keys()}
# 8 Calculating Hurricane Maximum Damage
# find highest damage inducing hurricane and its total cost
most_damaging = names[damages.index(max([damage for damage in damages if str(damage)[0] != 'D']))]
# 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
damages_dict = {grade: [hurricanes_dict[hurricane] for hurricane in hurricanes_dict if isinstance(hurricanes_dict[hurricane]['Damage'], float) and hurricanes_dict[hurricane]['Damage'] <= damage_scale[grade] and (hurricanes_dict[hurricane]['Damage'] > damage_scale[grade - 1] or grade == 0)] for grade in damage_scale.keys()}I have completed the exercise.
This is my solutions GIT HUB SOLUTION
Here is my code!
Hi all,
Here’s my solution for this project. Appreciate this one very much being a good level of difficulty and a step up to force you to think.
I really enjoy to practice my dictionary python skills with this exercise.
Hey this is my code for the project.
Can someone help me to update the link from Codeacademy platform to GitHub?
My project
Hi. For using github outside codecademy, you need to learn the basics of github, and create an account there. This course can help https://www.codecademy.com/learn/learn-github-introduction
Great link and thanks for sharing. As other have pointed out the link in the original post to set up Github is no longer functioning. I didn’t even know there’s a short course available for this (with a certification!).
This should be pinned and help others that have had issues with posting.
1 Like
Thank you Ramtob! I will check it out 
Finally, ready!
Hello everyone,
I am a very new beginner to coding and python. I am struggling with the first step of this exercise (converting hurricane damages).
I am looking through everyone’s code and not understanding what the -1 does. I know that it is relevant to the code and would not work without it. But can someone please explain to me what that means.
Thanks!
Hi, if abc is a Python list then abc[-1] returns the last item in the list.
abc = [2, 3, 4]
print(abc[-1])
will print “4”
So in the context of everybody’s code, the "damage[-1] would be referring to the M or B?
It is meaningless to refer to everybody’s code. Refer to a particular piece of code.
Here is my code https://www.codecademy.com/workspaces/66890c5ee36fd1c5eedc3cc7
Hi all,
This is my first time sharing. All comments are appreciated! thanks!